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MCP in the Web3 Ecosystem: A Comprehensive Review

· 49 min read
Dora Noda
Software Engineer

1. Definition and Origin of MCP in Web3 Context

The Model Context Protocol (MCP) is an open standard that connects AI assistants (like large language models) to external data sources, tools, and environments. Often described as a "USB-C port for AI" due to its universal plug-and-play nature, MCP was developed by Anthropic and first introduced in late November 2024. It emerged as a solution to break AI models out of isolation by securely bridging them with the “systems where data lives” – from databases and APIs to development environments and blockchains.

Originally an experimental side project at Anthropic, MCP quickly gained traction. By mid-2024, open-source reference implementations appeared, and by early 2025 it had become the de facto standard for agentic AI integration, with leading AI labs (OpenAI, Google DeepMind, Meta AI) adopting it natively. This rapid uptake was especially notable in the Web3 community. Blockchain developers saw MCP as a way to infuse AI capabilities into decentralized applications, leading to a proliferation of community-built MCP connectors for on-chain data and services. In fact, some analysts argue MCP may fulfill Web3’s original vision of a decentralized, user-centric internet in a more practical way than blockchain alone, by using natural language interfaces to empower users.

In summary, MCP is not a blockchain or token, but an open protocol born in the AI world that has rapidly been embraced within the Web3 ecosystem as a bridge between AI agents and decentralized data sources. Anthropic open-sourced the standard (with an initial GitHub spec and SDKs) and cultivated an open community around it. This community-driven approach set the stage for MCP’s integration into Web3, where it is now viewed as foundational infrastructure for AI-enabled decentralized applications.

2. Technical Architecture and Core Protocols

MCP operates on a lightweight client–server architecture with three principal roles:

  • MCP Host: The AI application or agent itself, which orchestrates requests. This could be a chatbot (Claude, ChatGPT) or an AI-powered app that needs external data. The host initiates interactions, asking for tools or information via MCP.
  • MCP Client: A connector component that the host uses to communicate with servers. The client maintains the connection, manages request/response messaging, and can handle multiple servers in parallel. For example, a developer tool like Cursor or VS Code’s agent mode can act as an MCP client bridging the local AI environment with various MCP servers.
  • MCP Server: A service that exposes some contextual data or functionality to the AI. Servers provide tools, resources, or prompts that the AI can use. In practice, an MCP server could interface with a database, a cloud app, or a blockchain node, and present a standardized set of operations to the AI. Each client-server pair communicates over its own channel, so an AI agent can tap multiple servers concurrently for different needs.

Core Primitives: MCP defines a set of standard message types and primitives that structure the AI-tool interaction. The three fundamental primitives are:

  • Tools: Discrete operations or functions the AI can invoke on a server. For instance, a “searchDocuments” tool or an “eth_call” tool. Tools encapsulate actions like querying an API, performing a calculation, or calling a smart contract function. The MCP client can request a list of available tools from a server and call them as needed.
  • Resources: Data endpoints that the AI can read from (or sometimes write to) via the server. These could be files, database entries, blockchain state (blocks, transactions), or any contextual data. The AI can list resources and retrieve their content through standard MCP messages (e.g. ListResources and ReadResource requests).
  • Prompts: Structured prompt templates or instructions that servers can provide to guide the AI’s reasoning. For example, a server might supply a formatting template or a pre-defined query prompt. The AI can request a list of prompt templates and use them to maintain consistency in how it interacts with that server.

Under the hood, MCP communications are typically JSON-based and follow a request-response pattern similar to RPC (Remote Procedure Call). The protocol’s specification defines messages like InitializeRequest, ListTools, CallTool, ListResources, etc., which ensure that any MCP-compliant client can talk to any MCP server in a uniform way. This standardization is what allows an AI agent to discover what it can do: upon connecting to a new server, it can inquire “what tools and data do you offer?” and then dynamically decide how to use them.

Security and Execution Model: MCP was designed with secure, controlled interactions in mind. The AI model itself doesn’t execute arbitrary code; it sends high-level intents (via the client) to the server, which then performs the actual operation (e.g., fetching data or calling an API) and returns results. This separation means sensitive actions (like blockchain transactions or database writes) can be sandboxed or require explicit user approval. For example, there are messages like Ping (to keep connections alive) and even a CreateMessageRequest which allows an MCP server to ask the client’s AI to generate a sub-response, typically gated by user confirmation. Features like authentication, access control, and audit logging are being actively developed to ensure MCP can be used safely in enterprise and decentralized environments (more on this in the Roadmap section).

In summary, MCP’s architecture relies on a standardized message protocol (with JSON-RPC style calls) that connects AI agents (hosts) to a flexible array of servers providing tools, data, and actions. This open architecture is model-agnostic and platform-agnostic – any AI agent can use MCP to talk to any resource, and any developer can create a new MCP server for a data source without needing to modify the AI’s core code. This plug-and-play extensibility is what makes MCP powerful in Web3: one can build servers for blockchain nodes, smart contracts, wallets, or oracles and have AI agents seamlessly integrate those capabilities alongside web2 APIs.

3. Use Cases and Applications of MCP in Web3

MCP unlocks a wide range of use cases by enabling AI-driven applications to access blockchain data and execute on-chain or off-chain actions in a secure, high-level way. Here are some key applications and problems it helps solve in the Web3 domain:

  • On-Chain Data Analysis and Querying: AI agents can query live blockchain state in real-time to provide insights or trigger actions. For example, an MCP server connected to an Ethereum node allows an AI to fetch account balances, read smart contract storage, trace transactions, or retrieve event logs on demand. This turns a chatbot or coding assistant into a blockchain explorer. Developers can ask an AI assistant questions like “What’s the current liquidity in Uniswap pool X?” or “Simulate this Ethereum transaction’s gas cost,” and the AI will use MCP tools to call an RPC node and get the answer from the live chain. This is far more powerful than relying on the AI’s training data or static snapshots.
  • Automated DeFi Portfolio Management: By combining data access and action tools, AI agents can manage crypto portfolios or DeFi positions. For instance, an “AI Vault Optimizer” could monitor a user’s positions across yield farms and automatically suggest or execute rebalancing strategies based on real-time market conditions. Similarly, an AI could act as a DeFi portfolio manager, adjusting allocations between protocols when risk or rates change. MCP provides the standard interface for the AI to read on-chain metrics (prices, liquidity, collateral ratios) and then invoke tools to execute transactions (like moving funds or swapping assets) if permitted. This can help users maximize yield or manage risk 24/7 in a way that would be hard to do manually.
  • AI-Powered User Agents for Transactions: Think of a personal AI assistant that can handle blockchain interactions for a user. With MCP, such an agent can integrate with wallets and DApps to perform tasks via natural language commands. For example, a user could say, "AI, send 0.5 ETH from my wallet to Alice" or "Stake my tokens in the highest-APY pool." The AI, through MCP, would use a secure wallet server (holding the user’s private key) to create and sign the transaction, and a blockchain MCP server to broadcast it. This scenario turns complex command-line or Metamask interactions into a conversational experience. It’s crucial that secure wallet MCP servers are used here, enforcing permissions and confirmations, but the end result is streamlining on-chain transactions through AI assistance.
  • Developer Assistants and Smart Contract Debugging: Web3 developers can leverage MCP-based AI assistants that are context-aware of blockchain infrastructure. For example, Chainstack’s MCP servers for EVM and Solana give AI coding copilots deep visibility into the developer’s blockchain environment. A smart contract engineer using an AI assistant (in VS Code or an IDE) can have the AI fetch the current state of a contract on a testnet, run a simulation of a transaction, or check logs – all via MCP calls to local blockchain nodes. This helps in debugging and testing contracts. The AI is no longer coding “blindly”; it can actually verify how code behaves on-chain in real time. This use case solves a major pain point by allowing AI to continuously ingest up-to-date docs (via a documentation MCP server) and to query the blockchain directly, reducing hallucinations and making suggestions far more accurate.
  • Cross-Protocol Coordination: Because MCP is a unified interface, a single AI agent can coordinate across multiple protocols and services simultaneously – something extremely powerful in Web3’s interconnected landscape. Imagine an autonomous trading agent that monitors various DeFi platforms for arbitrage. Through MCP, one agent could concurrently interface with Aave’s lending markets, a LayerZero cross-chain bridge, and an MEV (Miner Extractable Value) analytics service, all through a coherent interface. The AI could, in one “thought process,” gather liquidity data from Ethereum (via an MCP server on an Ethereum node), get price info or oracle data (via another server), and even invoke bridging or swapping operations. Previously, such multi-platform coordination would require complex custom-coded bots, but MCP gives a generalizable way for an AI to navigate the entire Web3 ecosystem as if it were one big data/resource pool. This could enable advanced use cases like cross-chain yield optimization or automated liquidation protection, where an AI moves assets or collateral across chains proactively.
  • AI Advisory and Support Bots: Another category is user-facing advisors in crypto applications. For instance, a DeFi help chatbot integrated into a platform like Uniswap or Compound could use MCP to pull in real-time info for the user. If a user asks, “What’s the best way to hedge my position?”, the AI can fetch current rates, volatility data, and the user’s portfolio details via MCP, then give a context-aware answer. Platforms are exploring AI-powered assistants embedded in wallets or dApps that can guide users through complex transactions, explain risks, and even execute sequences of steps with approval. These AI agents effectively sit on top of multiple Web3 services (DEXes, lending pools, insurance protocols), using MCP to query and command them as needed, thereby simplifying the user experience.
  • Beyond Web3 – Multi-Domain Workflows: Although our focus is Web3, it's worth noting MCP’s use cases extend to any domain where AI needs external data. It’s already being used to connect AI to things like Google Drive, Slack, GitHub, Figma, and more. In practice, a single AI agent could straddle Web3 and Web2: e.g., analyzing an Excel financial model from Google Drive, then suggesting on-chain trades based on that analysis, all in one workflow. MCP’s flexibility allows cross-domain automation (e.g., "schedule my meeting if my DAO vote passes, and email the results") that blends blockchain actions with everyday tools.

Problems Solved: The overarching problem MCP addresses is the lack of a unified interface for AI to interact with live data and services. Before MCP, if you wanted an AI to use a new service, you had to hand-code a plugin or integration for that specific service’s API, often in an ad-hoc way. In Web3 this was especially cumbersome – every blockchain or protocol has its own interfaces, and no AI could hope to support them all. MCP solves this by standardizing how the AI describes what it wants (natural language mapped to tool calls) and how services describe what they offer. This drastically reduces integration work. For example, instead of writing a custom plugin for each DeFi protocol, a developer can write one MCP server for that protocol (essentially annotating its functions in natural language). Any MCP-enabled AI (whether Claude, ChatGPT, or open-source models) can then immediately utilize it. This makes AI extensible in a plug-and-play fashion, much like how adding a new device via a universal port is easier than installing a new interface card.

In sum, MCP in Web3 enables AI agents to become first-class citizens of the blockchain world – querying, analyzing, and even transacting across decentralized systems, all through safe, standardized channels. This opens the door to more autonomous dApps, smarter user agents, and seamless integration of on-chain and off-chain intelligence.

4. Tokenomics and Governance Model

Unlike typical Web3 protocols, MCP does not have a native token or cryptocurrency. It is not a blockchain or a decentralized network on its own, but rather an open protocol specification (more akin to HTTP or JSON-RPC in spirit). Thus, there is no built-in tokenomics – no token issuance, staking, or fee model inherent to using MCP. AI applications and servers communicate via MCP without any cryptocurrency involved; for instance, an AI calling a blockchain via MCP might pay gas fees for the blockchain transaction, but MCP itself adds no extra token fee. This design reflects MCP’s origin in the AI community: it was introduced as a technical standard to improve AI-tool interactions, not as a tokenized project.

Governance of MCP is carried out in an open-source, community-driven fashion. After releasing MCP as an open standard, Anthropic signaled a commitment to collaborative development. A broad steering committee and working groups have formed to shepherd the protocol’s evolution. Notably, by mid-2025, major stakeholders like Microsoft and GitHub joined the MCP steering committee alongside Anthropic. This was announced at Microsoft Build 2025, indicating a coalition of industry players guiding MCP’s roadmap and standards decisions. The committee and maintainers work via an open governance process: proposals to change or extend MCP are typically discussed publicly (e.g. via GitHub issues and “SEP” – Standard Enhancement Proposal – guidelines). There is also an MCP Registry working group (with maintainers from companies like Block, PulseMCP, GitHub, and Anthropic) which exemplifies the multi-party governance. In early 2025, contributors from at least 9 different organizations collaborated to build a unified MCP server registry for discovery, demonstrating how development is decentralized across community members rather than controlled by one entity.

Since there is no token, governance incentives rely on the common interests of stakeholders (AI companies, cloud providers, blockchain developers, etc.) to improve the protocol for all. This is somewhat analogous to how W3C or IETF standards are governed, but with a faster-moving GitHub-centric process. For example, Microsoft and Anthropic worked together to design an improved authorization spec for MCP (integrating things like OAuth and single sign-on), and GitHub collaborated on the official MCP Registry service for listing available servers. These enhancements were contributed back to the MCP spec for everyone’s benefit.

It’s worth noting that while MCP itself is not tokenized, there are forward-looking ideas about layering economic incentives and decentralization on top of MCP. Some researchers and thought leaders in Web3 foresee the emergence of “MCP networks” – essentially decentralized networks of MCP servers and agents that use blockchain-like mechanisms for discovery, trust, and rewards. In such a scenario, one could imagine a token being used to reward those who run high-quality MCP servers (similar to how miners or node operators are incentivized). Capabilities like reputation ratings, verifiable computation, and node discovery could be facilitated by smart contracts or a blockchain, with a token driving honest behavior. This is still conceptual, but projects like MIT’s Namda (discussed later) are experimenting with token-based incentive mechanisms for networks of AI agents using MCP. If these ideas mature, MCP might intersect with on-chain tokenomics more directly, but as of 2025 the core MCP standard remains token-free.

In summary, MCP’s “governance model” is that of an open technology standard: collaboratively maintained by a community and a steering committee of experts, with no on-chain governance token. Decisions are guided by technical merit and broad consensus rather than coin-weighted voting. This distinguishes MCP from many Web3 protocols – it aims to fulfill Web3’s ideals (decentralization, interoperability, user empowerment) through open software and standards, not through a proprietary blockchain or token. In the words of one analysis, “the promise of Web3... can finally be realized not through blockchain and cryptocurrency, but through natural language and AI agents”, positioning MCP as a key enabler of that vision. That said, as MCP networks grow, we may see hybrid models where blockchain-based governance or incentive mechanisms augment the ecosystem – a space to watch closely.

5. Community and Ecosystem

The MCP ecosystem has grown explosively in a short time, spanning AI developers, open-source contributors, Web3 engineers, and major tech companies. It’s a vibrant community effort, with key contributors and partnerships including:

  • Anthropic: As the creator, Anthropic seeded the ecosystem by open-sourcing the MCP spec and several reference servers (for Google Drive, Slack, GitHub, etc.). Anthropic continues to lead development (for example, staff like Theodora Chu serve as MCP product managers, and Anthropic’s team contributes heavily to spec updates and community support). Anthropic’s openness attracted others to build on MCP rather than see it as a single-company tool.

  • Early Adopters (Block, Apollo, Zed, Replit, Codeium, Sourcegraph): In the first months after release, a wave of early adopters implemented MCP in their products. Block (formerly Square) integrated MCP to explore AI agentic systems in fintech – Block’s CTO praised MCP as an open bridge connecting AI to real-world applications. Apollo (likely Apollo GraphQL) also integrated MCP to allow AI access to internal data. Developer tool companies like Zed (code editor), Replit (cloud IDE), Codeium (AI coding assistant), and Sourcegraph (code search) each worked to add MCP support. For instance, Sourcegraph uses MCP so an AI coding assistant can retrieve relevant code from a repository in response to a question, and Replit’s IDE agents can pull in project-specific context. These early adopters gave MCP credibility and visibility.

  • Big Tech Endorsement – OpenAI, Microsoft, Google: In a notable turn, companies that are otherwise competitors aligned on MCP. OpenAI’s CEO Sam Altman publicly announced in March 2025 that OpenAI would add MCP support across its products (including ChatGPT’s desktop app), saying “People love MCP and we are excited to add support across our products”. This meant OpenAI’s Agent API and ChatGPT plugins would speak MCP, ensuring interoperability. Just weeks later, Google DeepMind’s CEO Demis Hassabis revealed that Google’s upcoming Gemini models and tools would support MCP, calling it a good protocol and an open standard for the “AI agentic era”. Microsoft not only joined the steering committee but partnered with Anthropic to build an official C# SDK for MCP to serve the enterprise developer community. Microsoft’s GitHub unit integrated MCP into GitHub Copilot (VS Code’s ‘Copilot Labs/Agents’ mode), enabling Copilot to use MCP servers for things like repository searching and running test cases. Additionally, Microsoft announced Windows 11 would expose certain OS functions (like file system access) as MCP servers so AI agents can interact with the operating system securely. The collaboration among OpenAI, Microsoft, Google, and Anthropic – all rallying around MCP – is extraordinary and underscores the community-over-competition ethos of this standard.

  • Web3 Developer Community: A number of blockchain developers and startups have embraced MCP. Several community-driven MCP servers have been created to serve blockchain use cases:

    • The team at Alchemy (a leading blockchain infrastructure provider) built an Alchemy MCP Server that offers on-demand blockchain analytics tools via MCP. This likely lets an AI get blockchain stats (like historical transactions, address activity) through Alchemy’s APIs using natural language.
    • Contributors developed a Bitcoin & Lightning Network MCP Server to interact with Bitcoin nodes and the Lightning payment network, enabling AI agents to read Bitcoin block data or even create Lightning invoices via standard tools.
    • The crypto media and education group Bankless created an Onchain MCP Server focused on Web3 financial interactions, possibly providing an interface to DeFi protocols (sending transactions, querying DeFi positions, etc.) for AI assistants.
    • Projects like Rollup.codes (a knowledge base for Ethereum Layer 2s) made an MCP server for rollup ecosystem info, so an AI can answer technical questions about rollups by querying this server.
    • Chainstack, a blockchain node provider, launched a suite of MCP servers (covered earlier) for documentation, EVM chain data, and Solana, explicitly marketing it as “putting your AI on blockchain steroids” for Web3 builders.

    Additionally, Web3-focused communities have sprung up around MCP. For example, PulseMCP and Goose are community initiatives referenced as helping build the MCP registry. We’re also seeing cross-pollination with AI agent frameworks: the LangChain community integrated adapters so that all MCP servers can be used as tools in LangChain-powered agents, and open-source AI platforms like Hugging Face TGI (text-generation-inference) are exploring MCP compatibility. The result is a rich ecosystem where new MCP servers are announced almost daily, serving everything from databases to IoT devices.

  • Scale of Adoption: The traction can be quantified to some extent. By February 2025 – barely three months after launch – over 1,000 MCP servers/connectors had been built by the community. This number has only grown, indicating thousands of integrations across industries. Mike Krieger (Anthropic’s Chief Product Officer) noted by spring 2025 that MCP had become a “thriving open standard with thousands of integrations and growing”. The official MCP Registry (launched in preview in Sept 2025) is cataloging publicly available servers, making it easier to discover tools; the registry’s open API allows anyone to search for, say, “Ethereum” or “Notion” and find relevant MCP connectors. This lowers the barrier for new entrants and further fuels growth.

  • Partnerships: We’ve touched on many implicit partnerships (Anthropic with Microsoft, etc.). To highlight a few more:

    • Anthropic & Slack: Anthropic partnered with Slack to integrate Claude with Slack’s data via MCP (Slack has an official MCP server, enabling AI to retrieve Slack messages or post alerts).
    • Cloud Providers: Amazon (AWS) and Google Cloud have worked with Anthropic to host Claude, and it’s likely they support MCP in those environments (e.g., AWS Bedrock might allow MCP connectors for enterprise data). While not explicitly in citations, these cloud partnerships are important for enterprise adoption.
    • Academic collaborations: The MIT and IBM research project Namda (discussed next) represents a partnership between academia and industry to push MCP’s limits in decentralized settings.
    • GitHub & VS Code: Partnership to enhance developer experience – e.g., VS Code’s team actively contributed to MCP (one of the registry maintainers is from VS Code team).
    • Numerous startups: Many AI startups (agent startups, workflow automation startups) are building on MCP instead of reinventing the wheel. This includes emerging Web3 AI startups looking to offer “AI as a DAO” or autonomous economic agents.

Overall, the MCP community is diverse and rapidly expanding. It includes core tech companies (for standards and base tooling), Web3 specialists (bringing blockchain knowledge and use cases), and independent developers (who often contribute connectors for their favorite apps or protocols). The ethos is collaborative. For example, security concerns about third-party MCP servers have prompted community discussions and contributions of best practices (e.g., Stacklok contributors working on security tooling for MCP servers). The community’s ability to iterate quickly (MCP saw several spec upgrades within months, adding features like streaming responses and better auth) is a testament to broad engagement.

In the Web3 ecosystem specifically, MCP has fostered a mini-ecosystem of “AI + Web3” projects. It’s not just a protocol to use; it’s catalyzing new ideas like AI-driven DAOs, on-chain governance aided by AI analysis, and cross-domain automation (like linking on-chain events to off-chain actions through AI). The presence of key Web3 figures – e.g., Zhivko Todorov of LimeChain stating “MCP represents the inevitable integration of AI and blockchain” – shows that blockchain veterans are actively championing it. Partnerships between AI and blockchain companies (such as the one between Anthropic and Block, or Microsoft’s Azure cloud making MCP easy to deploy alongside its blockchain services) hint at a future where AI agents and smart contracts work hand-in-hand.

One could say MCP has ignited the first genuine convergence of the AI developer community with the Web3 developer community. Hackathons and meetups now feature MCP tracks. As a concrete measure of ecosystem adoption: by mid-2025, OpenAI, Google, and Anthropic – collectively representing the majority of advanced AI models – all support MCP, and on the other side, leading blockchain infrastructure providers (Alchemy, Chainstack), crypto companies (Block, etc.), and decentralized projects are building MCP hooks. This two-sided network effect bodes well for MCP becoming a lasting standard.

6. Roadmap and Development Milestones

MCP’s development has been fast-paced. Here we outline the major milestones so far and the roadmap ahead as gleaned from official sources and community updates:

  • Late 2024 – Initial Release: On Nov 25, 2024, Anthropic officially announced MCP and open-sourced the specification and initial SDKs. Alongside the spec, they released a handful of MCP server implementations for common tools (Google Drive, Slack, GitHub, etc.) and added support in the Claude AI assistant (Claude Desktop app) to connect to local MCP servers. This marked the 1.0 launch of MCP. Early proof-of-concept integrations at Anthropic showed how Claude could use MCP to read files or query a SQL database in natural language, validating the concept.
  • Q1 2025 – Rapid Adoption and Iteration: In the first few months of 2025, MCP saw widespread industry adoption. By March 2025, OpenAI and other AI providers announced support (as described above). This period also saw spec evolution: Anthropic updated MCP to include streaming capabilities (allowing large results or continuous data streams to be sent incrementally). This update was noted in April 2025 with the C# SDK news, indicating MCP now supported features like chunked responses or real-time feed integration. The community also built reference implementations in various languages (Python, JavaScript, etc.) beyond Anthropic’s SDK, ensuring polyglot support.
  • Q2 2025 – Ecosystem Tooling and Governance: In May 2025, with Microsoft and GitHub joining the effort, there was a push for formalizing governance and enhancing security. At Build 2025, Microsoft unveiled plans for Windows 11 MCP integration and detailed a collaboration to improve authorization flows in MCP. Around the same time, the idea of an MCP Registry was introduced to index available servers (the initial brainstorming started in March 2025 according to the registry blog). The “standards track” process (SEP – Standard Enhancement Proposals) was established on GitHub, similar to Ethereum’s EIPs or Python’s PEPs, to manage contributions in an orderly way. Community calls and working groups (for security, registry, SDKs) started convening.
  • Mid 2025 – Feature Expansion: By mid-2025, the roadmap prioritized several key improvements:
    • Asynchronous and Long-Running Task Support: Plans to allow MCP to handle long operations without blocking the connection. For example, if an AI triggers a cloud job that takes minutes, the MCP protocol would support async responses or reconnection to fetch results.
    • Authentication & Fine-Grained Security: Developing fine-grained authorization mechanisms for sensitive actions. This includes possibly integrating OAuth flows, API keys, and enterprise SSO into MCP servers so that AI access can be safely managed. By mid-2025, guides and best practices for MCP security were in progress, given the security risks of allowing AI to invoke powerful tools. The goal is that, for instance, if an AI is to access a user’s private database via MCP, it should follow a secure authorization flow (with user consent) rather than just an open endpoint.
    • Validation and Compliance Testing: Recognizing the need for reliability, the community prioritized building compliance test suites and reference implementations. By ensuring all MCP clients/servers adhere to the spec (through automated testing), they aimed to prevent fragmentation. A reference server (likely an example with best practices for remote deployment and auth) was on the roadmap, as was a reference client application demonstrating full MCP usage with an AI.
    • Multimodality Support: Extending MCP beyond text to support modalities like image, audio, video data in the context. For example, an AI might request an image from an MCP server (say, a design asset or a diagram) or output an image. The spec discussion included adding support for streaming and chunked messages to handle large multimedia content interactively. Early work on “MCP Streaming” was already underway (to support things like live audio feeds or continuous sensor data to AI).
    • Central Registry & Discovery: The plan to implement a central MCP Registry service for server discovery was executed in mid-2025. By September 2025, the official MCP Registry was launched in preview. This registry provides a single source of truth for publicly available MCP servers, allowing clients to find servers by name, category, or capabilities. It’s essentially like an app store (but open) for AI tools. The design allows for public registries (a global index) and private ones (enterprise-specific), all interoperable via a shared API. The Registry also introduced a moderation mechanism to flag or delist malicious servers, with a community moderation model to maintain quality.
  • Late 2025 and Beyond – Toward Decentralized MCP Networks: While not “official” roadmap items yet, the trajectory points toward more decentralization and Web3 synergy:
    • Researchers are actively exploring how to add decentralized discovery, reputation, and incentive layers to MCP. The concept of an MCP Network (or “marketplace of MCP endpoints”) is being incubated. This might involve smart contract-based registries (so no single point of failure for server listings), reputation systems where servers/clients have on-chain identities and stake for good behavior, and possibly token rewards for running reliable MCP nodes.
    • Project Namda at MIT, which started in 2024, is a concrete step in this direction. By 2025, Namda had built a prototype distributed agent framework on MCP’s foundations, including features like dynamic node discovery, load balancing across agent clusters, and a decentralized registry using blockchain techniques. They even have experimental token-based incentives and provenance tracking for multi-agent collaborations. Milestones from Namda show that it’s feasible to have a network of MCP agents running across many machines with trustless coordination. If Namda’s concepts are adopted, we might see MCP evolve to incorporate some of these ideas (possibly through optional extensions or separate protocols layered on top).
    • Enterprise Hardening: On the enterprise side, by late 2025 we expect MCP to be integrated into major enterprise software offerings (Microsoft’s inclusion in Windows and Azure is one example). The roadmap includes enterprise-friendly features like SSO integration for MCP servers and robust access controls. The general availability of the MCP Registry and toolkits for deploying MCP at scale (e.g., within a corporate network) is likely by end of 2025.

To recap some key development milestones so far (timeline format for clarity):

  • Nov 2024: MCP 1.0 released (Anthropic).
  • Dec 2024 – Jan 2025: Community builds first wave of MCP servers; Anthropic releases Claude Desktop with MCP support; small-scale pilots by Block, Apollo, etc.
  • Feb 2025: 1000+ community MCP connectors achieved; Anthropic hosts workshops (e.g., at an AI summit, driving education).
  • Mar 2025: OpenAI announces support (ChatGPT Agents SDK).
  • Apr 2025: Google DeepMind announces support (Gemini will support MCP); Microsoft releases preview of C# SDK.
  • May 2025: Steering Committee expanded (Microsoft/GitHub); Build 2025 demos (Windows MCP integration).
  • Jun 2025: Chainstack launches Web3 MCP servers (EVM/Solana) for public use.
  • Jul 2025: MCP spec version updates (streaming, authentication improvements); official Roadmap published on MCP site.
  • Sep 2025: MCP Registry (preview) launched; likely MCP hits general availability in more products (Claude for Work, etc.).
  • Late 2025 (projected): Registry v1.0 live; security best-practice guides released; possibly initial experiments with decentralized discovery (Namda results).

The vision forward is that MCP becomes as ubiquitous and invisible as HTTP or JSON – a common layer that many apps use under the hood. For Web3, the roadmap suggests deeper fusion: where not only will AI agents use Web3 (blockchains) as sources or sinks of information, but Web3 infrastructure itself might start to incorporate AI agents (via MCP) as part of its operation (for example, a DAO might run an MCP-compatible AI to manage certain tasks, or oracles might publish data via MCP endpoints). The roadmap’s emphasis on things like verifiability and authentication hints that down the line, trust-minimized MCP interactions could be a reality – imagine AI outputs that come with cryptographic proofs, or an on-chain log of what tools an AI invoked for audit purposes. These possibilities blur the line between AI and blockchain networks, and MCP is at the heart of that convergence.

In conclusion, MCP’s development is highly dynamic. It has hit major early milestones (broad adoption and standardization within a year of launch) and continues to evolve rapidly with a clear roadmap emphasizing security, scalability, and discovery. The milestones achieved and planned ensure MCP will remain robust as it scales: addressing challenges like long-running tasks, secure permissions, and the sheer discoverability of thousands of tools. This forward momentum indicates that MCP is not a static spec but a growing standard, likely to incorporate more Web3-flavored features (decentralized governance of servers, incentive alignment) as those needs arise. The community is poised to adapt MCP to new use cases (multimodal AI, IoT, etc.), all while keeping an eye on the core promise: making AI more connected, context-aware, and user-empowering in the Web3 era.

7. Comparison with Similar Web3 Projects or Protocols

MCP’s unique blend of AI and connectivity means there aren’t many direct apples-to-apples equivalents, but it’s illuminating to compare it with other projects at the intersection of Web3 and AI or with analogous goals:

  • SingularityNET (AGI/X)Decentralized AI Marketplace: SingularityNET, launched in 2017 by Dr. Ben Goertzel and others, is a blockchain-based marketplace for AI services. It allows developers to monetize AI algorithms as services and users to consume those services, all facilitated by a token (AGIX) which is used for payments and governance. In essence, SingularityNET is trying to decentralize the supply of AI models by hosting them on a network where anyone can call an AI service in exchange for tokens. This differs from MCP fundamentally. MCP does not host or monetize AI models; instead, it provides a standard interface for AI (wherever it’s running) to access data/tools. One could imagine using MCP to connect an AI to services listed on SingularityNET, but SingularityNET itself focuses on the economic layer (who provides an AI service and how they get paid). Another key difference: Governance – SingularityNET has on-chain governance (via SingularityNET Enhancement Proposals (SNEPs) and AGIX token voting) to evolve its platform. MCP’s governance, by contrast, is off-chain and collaborative without a token. In summary, SingularityNET and MCP both strive for a more open AI ecosystem, but SingularityNET is about a tokenized network of AI algorithms, whereas MCP is about a protocol standard for AI-tool interoperability. They could complement: for example, an AI on SingularityNET could use MCP to fetch external data it needs. But SingularityNET doesn’t attempt to standardize tool use; it uses blockchain to coordinate AI services, while MCP uses software standards to let AI work with any service.
  • Fetch.ai (FET)Agent-Based Decentralized Platform: Fetch.ai is another project blending AI and blockchain. It launched its own proof-of-stake blockchain and framework for building autonomous agents that perform tasks and interact on a decentralized network. In Fetch’s vision, millions of “software agents” (representing people, devices, or organizations) can negotiate and exchange value, using FET tokens for transactions. Fetch.ai provides an agent framework (uAgents) and infrastructure for discovery and communication between agents on its ledger. For example, a Fetch agent might help optimize traffic in a city by interacting with other agents for parking and transport, or manage a supply chain workflow autonomously. How does this compare to MCP? Both deal with the concept of agents, but Fetch.ai’s agents are strongly tied to its blockchain and token economy – they live on the Fetch network and use on-chain logic. MCP agents (AI hosts) are model-driven (like an LLM) and not tied to any single network; MCP is content to operate over the internet or within a cloud setup, without requiring a blockchain. Fetch.ai tries to build a new decentralized AI economy from the ground up (with its own ledger for trust and transactions), whereas MCP is layer-agnostic – it piggybacks on existing networks (could be used over HTTPS, or even on top of a blockchain if needed) to enable AI interactions. One might say Fetch is more about autonomous economic agents and MCP about smart tool-using agents. Interestingly, these could intersect: an autonomous agent on Fetch.ai might use MCP to interface with off-chain resources or other blockchains. Conversely, one could use MCP to build multi-agent systems that leverage different blockchains (not just one). In practice, MCP has seen faster adoption because it didn’t require its own network – it works with Ethereum, Solana, Web2 APIs, etc., out of the box. Fetch.ai’s approach is more heavyweight, creating an entire ecosystem that participants must join (and acquire tokens) to use. In sum, Fetch.ai vs MCP: Fetch is a platform with its own token/blockchain for AI agents, focusing on interoperability and economic exchanges between agents, while MCP is a protocol that AI agents (in any environment) can use to plug into tools and data. Their goals overlap in enabling AI-driven automation, but they tackle different layers of the stack and have very different architectural philosophies (closed ecosystem vs open standard).
  • Chainlink and Decentralized OraclesConnecting Blockchains to Off-Chain Data: Chainlink is not an AI project, but it’s highly relevant as a Web3 protocol solving a complementary problem: how to connect blockchains with external data and computation. Chainlink is a decentralized network of nodes (oracles) that fetch, verify, and deliver off-chain data to smart contracts in a trust-minimized way. For example, Chainlink oracles provide price feeds to DeFi protocols or call external APIs on behalf of smart contracts via Chainlink Functions. Comparatively, MCP connects AI models to external data/tools (some of which might be blockchains). One could say Chainlink brings data into blockchains, while MCP brings data into AI. There is a conceptual parallel: both establish a bridge between otherwise siloed systems. Chainlink focuses on reliability, decentralization, and security of data fed on-chain (solving the “oracle problem” of single point of failure). MCP focuses on flexibility and standardization of how AI can access data (solving the “integration problem” for AI agents). They operate in different domains (smart contracts vs AI assistants), but one might compare MCP servers to oracles: an MCP server for price data might call the same APIs a Chainlink node does. The difference is the consumer – in MCP’s case, the consumer is an AI or user-facing assistant, not a deterministic smart contract. Also, MCP does not inherently provide the trust guarantees that Chainlink does (MCP servers can be centralized or community-run, with trust managed at the application level). However, as mentioned earlier, ideas to decentralize MCP networks could borrow from oracle networks – e.g., multiple MCP servers could be queried and results cross-checked to ensure an AI isn’t fed bad data, similar to how multiple Chainlink nodes aggregate a price. In short, Chainlink vs MCP: Chainlink is Web3 middleware for blockchains to consume external data, MCP is AI middleware for models to consume external data (which could include blockchain data). They address analogous needs in different realms and could even complement: an AI using MCP might fetch a Chainlink-provided data feed as a reliable resource, and conversely, an AI could serve as a source of analysis that a Chainlink oracle brings on-chain (though that latter scenario would raise questions of verifiability).
  • ChatGPT Plugins / OpenAI Functions vs MCPAI Tool Integration Approaches: While not Web3 projects, a quick comparison is warranted because ChatGPT plugins and OpenAI’s function calling feature also connect AI to external tools. ChatGPT plugins use an OpenAPI specification provided by a service, and the model can then call those APIs following the spec. The limitations are that it’s a closed ecosystem (OpenAI-approved plugins running on OpenAI’s servers) and each plugin is a siloed integration. OpenAI’s newer “Agents” SDK is closer to MCP in concept, letting developers define tools/functions that an AI can use, but initially it was specific to OpenAI’s ecosystem. LangChain similarly provided a framework to give LLMs tools in code. MCP differs by offering an open, model-agnostic standard for this. As one analysis put it, LangChain created a developer-facing standard (a Python interface) for tools, whereas MCP creates a model-facing standard – an AI agent can discover and use any MCP-defined tool at runtime without custom code. In practical terms, MCP’s ecosystem of servers grew larger and more diverse than the ChatGPT plugin store within months. And rather than each model having its own plugin format (OpenAI had theirs, others had different ones), many are coalescing around MCP. OpenAI itself signaled support for MCP, essentially aligning their function approach with the broader standard. So, comparing OpenAI Plugins to MCP: plugins are a curated, centralized approach, while MCP is a decentralized, community-driven approach. In a Web3 mindset, MCP is more “open source and permissionless” whereas proprietary plugin ecosystems are more closed. This makes MCP analogous to the ethos of Web3 even though it’s not a blockchain – it enables interoperability and user control (you could run your own MCP server for your data, instead of giving it all to one AI provider). This comparison shows why many consider MCP as having more long-term potential: it’s not locked to one vendor or one model.
  • Project Namda and Decentralized Agent Frameworks: Namda deserves a separate note because it explicitly combines MCP with Web3 concepts. As described earlier, Namda (Networked Agent Modular Distributed Architecture) is an MIT/IBM initiative started in 2024 to build a scalable, distributed network of AI agents using MCP as the communication layer. It treats MCP as the messaging backbone (since MCP uses standard JSON-RPC-like messages, it fit well for inter-agent comms), and then adds layers for dynamic discovery, fault tolerance, and verifiable identities using blockchain-inspired techniques. Namda’s agents can be anywhere (cloud, edge devices, etc.), but a decentralized registry (somewhat like a DHT or blockchain) keeps track of them and their capabilities in a tamper-proof way. They even explore giving agents tokens to incentivize cooperation or resource sharing. In essence, Namda is an experiment in what a “Web3 version of MCP” might look like. It’s not a widely deployed project yet, but it’s one of the closest “similar protocols” in spirit. If we view Namda vs MCP: Namda uses MCP (so it’s not competing standards), but extends it with a protocol for networking and coordinating multiple agents in a trust-minimized manner. One could compare Namda to frameworks like Autonolas or Multi-Agent Systems (MAS) that the crypto community has seen, but those often lacked a powerful AI component or a common protocol. Namda + MCP together showcase how a decentralized agent network could function, with blockchain providing identity, reputation, and possibly token incentives, and MCP providing the agent communication and tool-use.

In summary, MCP stands apart from most prior Web3 projects: it did not start as a crypto project at all, yet it rapidly intersects with Web3 because it solves complementary problems. Projects like SingularityNET and Fetch.ai aimed to decentralize AI compute or services using blockchain; MCP instead standardizes AI integration with services, which can enhance decentralization by avoiding platform lock-in. Oracle networks like Chainlink solved data delivery to blockchain; MCP solves data delivery to AI (including blockchain data). If Web3’s core ideals are decentralization, interoperability, and user empowerment, MCP is attacking the interoperability piece in the AI realm. It’s even influencing those older projects – for instance, there is nothing stopping SingularityNET from making its AI services available via MCP servers, or Fetch agents from using MCP to talk to external systems. We might well see a convergence where token-driven AI networks use MCP as their lingua franca, marrying the incentive structure of Web3 with the flexibility of MCP.

Finally, if we consider market perception: MCP is often touted as doing for AI what Web3 hoped to do for the internet – break silos and empower users. This has led some to nickname MCP informally as “Web3 for AI” (even when no blockchain is involved). However, it’s important to recognize MCP is a protocol standard, whereas most Web3 projects are full-stack platforms with economic layers. In comparisons, MCP usually comes out as a more lightweight, universal solution, while blockchain projects are heavier, specialized solutions. Depending on use case, they can complement rather than strictly compete. As the ecosystem matures, we might see MCP integrated into many Web3 projects as a module (much like how HTTP or JSON are ubiquitous), rather than as a rival project.

8. Public Perception, Market Traction, and Media Coverage

Public sentiment toward MCP has been overwhelmingly positive in both the AI and Web3 communities, often bordering on enthusiastic. Many see it as a game-changer that arrived quietly but then took the industry by storm. Let’s break down the perception, traction, and notable media narratives:

Market Traction and Adoption Metrics: By mid-2025, MCP achieved a level of adoption rare for a new protocol. It’s backed by virtually all major AI model providers (Anthropic, OpenAI, Google, Meta) and supported by big tech infrastructure (Microsoft, GitHub, AWS etc.), as detailed earlier. This alone signals to the market that MCP is likely here to stay (akin to how broad backing propelled TCP/IP or HTTP in early internet days). On the Web3 side, the traction is evident in developer behavior: hackathons started featuring MCP projects, and many blockchain dev tools now mention MCP integration as a selling point. The stat of “1000+ connectors in a few months” and Mike Krieger’s “thousands of integrations” quote are often cited to illustrate how rapidly MCP caught on. This suggests strong network effects – the more tools available via MCP, the more useful it is, prompting more adoption (a positive feedback loop). VCs and analysts have noted that MCP achieved in under a year what earlier “AI interoperability” attempts failed to do over several years, largely due to timing (riding the wave of interest in AI agents) and being open-source. In Web3 media, traction is sometimes measured in terms of developer mindshare and integration into projects, and MCP scores high on both now.

Public Perception in AI and Web3 Communities: Initially, MCP flew under the radar when first announced (late 2024). But by early 2025, as success stories emerged, perception shifted to excitement. AI practitioners saw MCP as the “missing puzzle piece” for making AI agents truly useful beyond toy examples. Web3 builders, on the other hand, saw it as a bridge to finally incorporate AI into dApps without throwing away decentralization – an AI can use on-chain data without needing a centralized oracle, for instance. Thought leaders have been singing praises: for example, Jesus Rodriguez (a prominent Web3 AI writer) wrote in CoinDesk that MCP may be “one of the most transformative protocols for the AI era and a great fit for Web3 architectures”. Rares Crisan in a Notable Capital blog argued that MCP could deliver on Web3’s promise where blockchain alone struggled, by making the internet more user-centric and natural to interact with. These narratives frame MCP as revolutionary yet practical – not just hype.

To be fair, not all commentary is uncritical. Some AI developers on forums like Reddit have pointed out that MCP “doesn’t do everything” – it’s a communication protocol, not an out-of-the-box agent or reasoning engine. For instance, one Reddit discussion titled “MCP is a Dead-End Trap” argued that MCP by itself doesn’t manage agent cognition or guarantee quality; it still requires good agent design and safety controls. This view suggests MCP could be overhyped as a silver bullet. However, these criticisms are more about tempering expectations than rejecting MCP’s usefulness. They emphasize that MCP solves tool connectivity but one must still build robust agent logic (i.e., MCP doesn’t magically create an intelligent agent, it equips one with tools). The consensus though is that MCP is a big step forward, even among cautious voices. Hugging Face’s community blog noted that while MCP isn’t a solve-it-all, it is a major enabler for integrated, context-aware AI, and developers are rallying around it for that reason.

Media Coverage: MCP has received significant coverage across both mainstream tech media and niche blockchain media:

  • TechCrunch has run multiple stories. They covered the initial concept (“Anthropic proposes a new way to connect data to AI chatbots”) around launch in 2024. In 2025, TechCrunch highlighted each big adoption moment: OpenAI’s support, Google’s embrace, Microsoft/GitHub’s involvement. These articles often emphasize the industry unity around MCP. For example, TechCrunch quoted Sam Altman’s endorsement and noted the rapid shift from rival standards to MCP. In doing so, they portrayed MCP as the emerging standard similar to how no one wanted to be left out of the internet protocols in the 90s. Such coverage in a prominent outlet signaled to the broader tech world that MCP is important and real, not just a fringe open-source project.
  • CoinDesk and other crypto publications latched onto the Web3 angle. CoinDesk’s opinion piece by Rodriguez (July 2025) is often cited; it painted a futuristic picture where every blockchain could be an MCP server and new MCP networks might run on blockchains. It connected MCP to concepts like decentralized identity, authentication, and verifiability – speaking the language of the blockchain audience and suggesting MCP could be the protocol that truly melds AI with decentralized frameworks. Cointelegraph, Bankless, and others have also discussed MCP in context of “AI agents & DeFi” and similar topics, usually optimistic about the possibilities (e.g., Bankless had a piece on using MCP to let an AI manage on-chain trades, and included a how-to for their own MCP server).
  • Notable VC Blogs / Analyst Reports: The Notable Capital blog post (July 2025) is an example of venture analysis drawing parallels between MCP and the evolution of web protocols. It essentially argues MCP could do for Web3 what HTTP did for Web1 – providing a new interface layer (natural language interface) that doesn’t replace underlying infrastructure but makes it usable. This kind of narrative is compelling and has been echoed in panels and podcasts. It positions MCP not as competing with blockchain, but as the next layer of abstraction that finally allows normal users (via AI) to harness blockchain and web services easily.
  • Developer Community Buzz: Outside formal articles, MCP’s rise can be gauged by its presence in developer discourse – conference talks, YouTube channels, newsletters. For instance, there have been popular blog posts like “MCP: The missing link for agentic AI?” on sites like Runtime.news, and newsletters (e.g., one by AI researcher Nathan Lambert) discussing practical experiments with MCP and how it compares to other tool-use frameworks. The general tone is curiosity and excitement: developers share demos of hooking up AI to their home automation or crypto wallet with just a few lines using MCP servers, something that felt sci-fi not long ago. This grassroots excitement is important because it shows MCP has mindshare beyond just corporate endorsements.
  • Enterprise Perspective: Media and analysts focusing on enterprise AI also note MCP as a key development. For example, The New Stack covered how Anthropic added support for remote MCP servers in Claude for enterprise use. The angle here is that enterprises can use MCP to connect their internal knowledge bases and systems to AI safely. This matters for Web3 too as many blockchain companies are enterprises themselves and can leverage MCP internally (for instance, a crypto exchange could use MCP to let an AI analyze internal transaction logs for fraud detection).

Notable Quotes and Reactions: A few are worth highlighting as encapsulating public perception:

  • “Much like HTTP revolutionized web communications, MCP provides a universal framework... replacing fragmented integrations with a single protocol.” – CoinDesk. This comparison to HTTP is powerful; it frames MCP as infrastructure-level innovation.
  • “MCP has [become a] thriving open standard with thousands of integrations and growing. LLMs are most useful when connecting to the data you already have...” – Mike Krieger (Anthropic). This is an official confirmation of both traction and the core value proposition, which has been widely shared on social media.
  • “The promise of Web3... can finally be realized... through natural language and AI agents. ...MCP is the closest thing we've seen to a real Web3 for the masses.” – Notable Capital. This bold statement resonates with those frustrated by the slow UX improvements in crypto; it suggests AI might crack the code of mainstream adoption by abstracting complexity.

Challenges and Skepticism: While enthusiasm is high, the media has also discussed challenges:

  • Security Concerns: Outlets like The New Stack or security blogs have raised that allowing AI to execute tools can be dangerous if not sandboxed. What if a malicious MCP server tried to get an AI to perform a harmful action? The LimeChain blog explicitly warns of “significant security risks” with community-developed MCP servers (e.g., a server that handles private keys must be extremely secure). These concerns have been echoed in discussions: essentially, MCP expands AI’s capabilities, but with power comes risk. The community’s response (guides, auth mechanisms) has been covered as well, generally reassuring that mitigations are being built. Still, any high-profile misuse of MCP (say an AI triggered an unintended crypto transfer) would affect perception, so media is watchful on this front.
  • Performance and Cost: Some analysts note that using AI agents with tools could be slower or more costly than directly calling an API (because the AI might need multiple back-and-forth steps to get what it needs). In high-frequency trading or on-chain execution contexts, that latency could be problematic. For now, these are seen as technical hurdles to optimize (through better agent design or streaming), rather than deal-breakers.
  • Hype management: As with any trending tech, there’s a bit of hype. A few voices caution not to declare MCP the solution to everything. For instance, the Hugging Face article asks “Is MCP a silver bullet?” and answers no – developers still need to handle context management, and MCP works best in combination with good prompting and memory strategies. Such balanced takes are healthy in the discourse.

Overall Media Sentiment: The narrative that emerges is largely hopeful and forward-looking:

  • MCP is seen as a practical tool delivering real improvements now (so not vaporware), which media underscore by citing working examples: Claude reading files, Copilot using MCP in VSCode, an AI completing a Solana transaction in a demo, etc..
  • It’s also portrayed as a strategic linchpin for the future of both AI and Web3. Media often conclude that MCP or things like it will be essential for “decentralized AI” or “Web4” or whatever term one uses for the next-gen web. There’s a sense that MCP opened a door, and now innovation is flowing through – whether it's Namda’s decentralized agents or enterprises connecting legacy systems to AI, many future storylines trace back to MCP’s introduction.

In the market, one could gauge traction by the formation of startups and funding around the MCP ecosystem. Indeed, there are rumors/reports of startups focusing on “MCP marketplaces” or managed MCP platforms getting funding (Notable Capital writing about it suggests VC interest). We can expect media to start covering those tangentially – e.g., “Startup X uses MCP to let your AI manage your crypto portfolio – raises $Y million”.

Conclusion of Perception: By late 2025, MCP enjoys a reputation as a breakthrough enabling technology. It has strong advocacy from influential figures in both AI and crypto. The public narrative has evolved from “here’s a neat tool” to “this could be foundational for the next web”. Meanwhile, practical coverage confirms it’s working and being adopted, lending credibility. Provided the community continues addressing challenges (security, governance at scale) and no major disasters occur, MCP’s public image is likely to remain positive or even become iconic as “the protocol that made AI and Web3 play nice together.”

Media will likely keep a close eye on:

  • Success stories (e.g., if a major DAO implements an AI treasurer via MCP, or a government uses MCP for open data AI systems).
  • Any security incidents (to evaluate risk).
  • The evolution of MCP networks and whether any token or blockchain component officially enters the picture (which would be big news bridging AI and crypto even more tightly).

As of now, however, the coverage can be summed up by a line from CoinDesk: “The combination of Web3 and MCP might just be a new foundation for decentralized AI.” – a sentiment that captures both the promise and the excitement surrounding MCP in the public eye.

References:

  • Anthropic News: "Introducing the Model Context Protocol," Nov 2024
  • LimeChain Blog: "What is MCP and How Does It Apply to Blockchains?" May 2025
  • Chainstack Blog: "MCP for Web3 Builders: Solana, EVM and Documentation," June 2025
  • CoinDesk Op-Ed: "The Protocol of Agents: Web3’s MCP Potential," Jul 2025
  • Notable Capital: "Why MCP Represents the Real Web3 Opportunity," Jul 2025
  • TechCrunch: "OpenAI adopts Anthropic’s standard…", Mar 26, 2025
  • TechCrunch: "Google to embrace Anthropic’s standard…", Apr 9, 2025
  • TechCrunch: "GitHub, Microsoft embrace… (MCP steering committee)", May 19, 2025
  • Microsoft Dev Blog: "Official C# SDK for MCP," Apr 2025
  • Hugging Face Blog: "#14: What Is MCP, and Why Is Everyone Talking About It?" Mar 2025
  • Messari Research: "Fetch.ai Profile," 2023
  • Medium (Nu FinTimes): "Unveiling SingularityNET," Mar 2024

Connecting AI and Web3 through MCP: A Panoramic Analysis

· 43 min read
Dora Noda
Software Engineer

Introduction

AI and Web3 are converging in powerful ways, with AI general interfaces now envisioned as a connective tissue for the decentralized web. A key concept emerging from this convergence is MCP, which variously stands for “Model Context Protocol” (as introduced by Anthropic) or is loosely described as a Metaverse Connection Protocol in broader discussions. In essence, MCP is a standardized framework that lets AI systems interface with external tools and networks in a natural, secure way – potentially “plugging in” AI agents to every corner of the Web3 ecosystem. This report provides a comprehensive analysis of how AI general interfaces (like large language model agents and neural-symbolic systems) could connect everything in the Web3 world via MCP, covering the historical background, technical architecture, industry landscape, risks, and future potential.

1. Development Background

1.1 Web3’s Evolution and Unmet Promises

The term “Web3” was coined around 2014 to describe a blockchain-powered decentralized web. The vision was ambitious: a permissionless internet centered on user ownership. Enthusiasts imagined replacing Web2’s centralized infrastructure with blockchain-based alternatives – e.g. Ethereum Name Service (for DNS), Filecoin or IPFS (for storage), and DeFi for financial rails. In theory, this would wrest control from Big Tech platforms and give individuals self-sovereignty over data, identity, and assets.

Reality fell short. Despite years of development and hype, the mainstream impact of Web3 remained marginal. Average internet users did not flock to decentralized social media or start managing private keys. Key reasons included poor user experience, slow and expensive transactions, high-profile scams, and regulatory uncertainty. The decentralized “ownership web” largely “failed to materialize” beyond a niche community. By the mid-2020s, even crypto proponents admitted that Web3 had not delivered a paradigm shift for the average user.

Meanwhile, AI was undergoing a revolution. As capital and developer talent pivoted from crypto to AI, transformative advances in deep learning and foundation models (GPT-3, GPT-4, etc.) captured public imagination. Generative AI demonstrated clear utility – producing content, code, and decisions – in a way crypto applications had struggled to do. In fact, the impact of large language models in just a couple of years starkly outpaced a decade of blockchain’s user adoption. This contrast led some to quip that “Web3 was wasted on crypto” and that the real Web 3.0 is emerging from the AI wave.

1.2 The Rise of AI General Interfaces

Over decades, user interfaces evolved from static web pages (Web1.0) to interactive apps (Web2.0) – but always within the confines of clicking buttons and filling forms. With modern AI, especially large language models (LLMs), a new interface paradigm is here: natural language. Users can simply express intent in plain language and have AI systems execute complex actions across many domains. This shift is so profound that some suggest redefining “Web 3.0” as the era of AI-driven agents (“the Agentic Web”) rather than the earlier blockchain-centric definition.

However, early experiments with autonomous AI agents exposed a critical bottleneck. These agents – e.g. prototypes like AutoGPT – could generate text or code, but they lacked a robust way to communicate with external systems and each other. There was “no common AI-native language” for interoperability. Each integration with a tool or data source was a bespoke hack, and AI-to-AI interaction had no standard protocol. In practical terms, an AI agent might have great reasoning ability but fail at executing tasks that required using web apps or on-chain services, simply because it didn’t know how to talk to those systems. This mismatch – powerful brains, primitive I/O – was akin to having super-smart software stuck behind a clumsy GUI.

1.3 Convergence and the Emergence of MCP

By 2024, it became evident that for AI to reach its full potential (and for Web3 to fulfill its promise), a convergence was needed: AI agents require seamless access to the capabilities of Web3 (decentralized apps, contracts, data), and Web3 needs more intelligence and usability, which AI can provide. This is the context in which MCP (Model Context Protocol) was born. Introduced by Anthropic in late 2024, MCP is an open standard for AI-tool communication that feels natural to LLMs. It provides a structured, discoverable way for AI “hosts” (like ChatGPT, Claude, etc.) to find and use a variety of external tools and resources via MCP servers. In other words, MCP is a common interface layer enabling AI agents to plug into web services, APIs, and even blockchain functions, without custom-coding each integration.

Think of MCP as “the USB-C of AI interfaces”. Just as USB-C standardized how devices connect (so you don’t need different cables for each device), MCP standardizes how AI agents connect to tools and data. Rather than hard-coding different API calls for every service (Slack vs. Gmail vs. Ethereum node), a developer can implement the MCP spec once, and any MCP-compatible AI can understand how to use that service. Major AI players quickly saw the importance: Anthropic open-sourced MCP, and companies like OpenAI and Google are building support for it in their models. This momentum suggests MCP (or similar “Meta Connectivity Protocols”) could become the backbone that finally connects AI and Web3 in a scalable way.

Notably, some technologists argue that this AI-centric connectivity is the real realization of Web3.0. In Simba Khadder’s words, “MCP aims to standardize an API between LLMs and applications,” akin to how REST APIs enabled Web 2.0 – meaning Web3’s next era might be defined by intelligent agent interfaces rather than just blockchains. Instead of decentralization for its own sake, the convergence with AI could make decentralization useful, by hiding complexity behind natural language and autonomous agents. The remainder of this report delves into how, technically and practically, AI general interfaces (via protocols like MCP) can connect everything in the Web3 world.

2. Technical Architecture: AI Interfaces Bridging Web3 Technologies

Embedding AI agents into the Web3 stack requires integration at multiple levels: blockchain networks and smart contracts, decentralized storage, identity systems, and token-based economies. AI general interfaces – from large foundation models to hybrid neural-symbolic systems – can serve as a “universal adapter” connecting these components. Below, we analyze the architecture of such integration:

** Figure: A conceptual diagram of MCP’s architecture, showing how AI hosts (LLM-based apps like Claude or ChatGPT) use an MCP client to plug into various MCP servers. Each server provides a bridge to some external tool or service (e.g. Slack, Gmail, calendars, or local data), analogous to peripherals connecting via a universal hub. This standardized MCP interface lets AI agents access remote services and on-chain resources through one common protocol.**

2.1 AI Agents as Web3 Clients (Integrating with Blockchains)

At the core of Web3 are blockchains and smart contracts – decentralized state machines that can enforce logic in a trustless manner. How can an AI interface engage with these? There are two directions to consider:

  • AI reading from blockchain: An AI agent may need on-chain data (e.g. token prices, user’s asset balance, DAO proposals) as context for its decisions. Traditionally, retrieving blockchain data requires interfacing with node RPC APIs or subgraph databases. With a framework like MCP, an AI can query a standardized “blockchain data” MCP server to fetch live on-chain information. For example, an MCP-enabled agent could ask for the latest transaction volume of a certain token, or the state of a smart contract, and the MCP server would handle the low-level details of connecting to the blockchain and return the data in a format the AI can use. This increases interoperability by decoupling the AI from any specific blockchain’s API format.

  • AI writing to blockchain: More powerfully, AI agents can execute smart contract calls or transactions through Web3 integrations. An AI could, for instance, autonomously execute a trade on a decentralized exchange or adjust parameters in a smart contract if certain conditions are met. This is achieved by the AI invoking an MCP server that wraps blockchain transaction functionality. One concrete example is the thirdweb MCP server for EVM chains, which allows any MCP-compatible AI client to interact with Ethereum, Polygon, BSC, etc. by abstracting away chain-specific mechanics. Using such a tool, an AI agent could trigger on-chain actions “without human intervention”, enabling autonomous dApps – for instance, an AI-driven DeFi vault that rebalances itself by signing transactions when market conditions change.

Under the hood, these interactions still rely on wallets, keys, and gas fees, but the AI interface can be given controlled access to a wallet (with proper security sandboxes) to perform the transactions. Oracles and cross-chain bridges also come into play: Oracle networks like Chainlink serve as a bridge between AI and blockchains, allowing AI outputs to be fed on-chain in a trustworthy way. Chainlink’s Cross-Chain Interoperability Protocol (CCIP), for example, could enable an AI model deemed reliable to trigger multiple contracts across different chains simultaneously on behalf of a user. In summary, AI general interfaces can act as a new type of Web3 client – one that can both consume blockchain data and produce blockchain transactions through standardized protocols.

2.2 Neural-Symbolic Synergy: Combining AI Reasoning with Smart Contracts

One intriguing aspect of AI-Web3 integration is the potential for neural-symbolic architectures that combine the learning ability of AI (neural nets) with the rigorous logic of smart contracts (symbolic rules). In practice, this could mean AI agents handling unstructured decision-making and passing certain tasks to smart contracts for verifiable execution. For instance, an AI might analyze market sentiment (a fuzzy task), but then execute trades via a deterministic smart contract that follows pre-set risk rules. The MCP framework and related standards make such hand-offs feasible by giving the AI a common interface to call contract functions or to query a DAO’s rules before acting.

A concrete example is SingularityNET’s AI-DSL (AI Domain Specific Language), which aims to standardize communication between AI agents on their decentralized network. This can be seen as a step toward neural-symbolic integration: a formal language (symbolic) for agents to request AI services or data from each other. Similarly, projects like DeepMind’s AlphaCode or others could eventually be connected so that smart contracts call AI models for on-chain problem solving. Although running large AI models directly on-chain is impractical today, hybrid approaches are emerging: e.g. certain blockchains allow verification of ML computations via zero-knowledge proofs or trusted execution, enabling on-chain verification of off-chain AI results. In summary, the technical architecture envisions AI systems and blockchain smart contracts as complementary components, orchestrated via common protocols: AI handles perception and open-ended tasks, while blockchains provide integrity, memory, and enforcement of agreed rules.

2.3 Decentralized Storage and Data for AI

AI thrives on data, and Web3 offers new paradigms for data storage and sharing. Decentralized storage networks (like IPFS/Filecoin, Arweave, Storj, etc.) can serve as both repositories for AI model artifacts and sources of training data, with blockchain-based access control. An AI general interface, through MCP or similar, could fetch files or knowledge from decentralized storage just as easily as from a Web2 API. For example, an AI agent might pull a dataset from Ocean Protocol’s market or an encrypted file from a distributed storage, if it has the proper keys or payments.

Ocean Protocol in particular has positioned itself as an “AI data economy” platform – using blockchain to tokenize data and even AI services. In Ocean, datasets are represented by datatokens which gate access; an AI agent could obtain a datatoken (perhaps by paying with crypto or via some access right) and then use an Ocean MCP server to retrieve the actual data for analysis. Ocean’s goal is to unlock “dormant data” for AI, incentivizing sharing while preserving privacy. Thus, a Web3-connected AI might tap into a vast, decentralized corpus of information – from personal data vaults to open government data – that was previously siloed. The blockchain ensures that usage of the data is transparent and can be fairly rewarded, fueling a virtuous cycle where more data becomes available to AI and more AI contributions (like trained models) can be monetized.

Decentralized identity systems also play a role here (discussed more in the next subsection): they can help control who or what is allowed to access certain data. For instance, a medical AI agent could be required to present a verifiable credential (on-chain proof of compliance with HIPAA or similar) before being allowed to decrypt a medical dataset from a patient’s personal IPFS storage. In this way, the technical architecture ensures data flows to AI where appropriate, but with on-chain governance and audit trails to enforce permissions.

2.4 Identity and Agent Management in a Decentralized Environment

When autonomous AI agents operate in an open ecosystem like Web3, identity and trust become paramount. Decentralized identity (DID) frameworks provide a way to establish digital identities for AI agents that can be cryptographically verified. Each agent (or the human/organization deploying it) can have a DID and associated verifiable credentials that specify its attributes and permissions. For example, an AI trading bot could carry a credential issued by a regulatory sandbox certifying it may operate within certain risk limits, or an AI content moderator could prove it was created by a trusted organization and has undergone bias testing.

Through on-chain identity registries and reputation systems, the Web3 world can enforce accountability for AI actions. Every transaction an AI agent performs can be traced back to its ID, and if something goes wrong, the credentials tell you who built it or who is responsible. This addresses a critical challenge: without identity, a malicious actor could spin up fake AI agents to exploit systems or spread misinformation, and no one could tell bots apart from legitimate services. Decentralized identity helps mitigate that by enabling robust authentication and distinguishing authentic AI agents from spoofs.

In practice, an AI interface integrated with Web3 would use identity protocols to sign its actions and requests. For instance, when an AI agent calls an MCP server to use a tool, it might include a token or signature tied to its decentralized identity, so the server can verify the call is from an authorized agent. Blockchain-based identity systems (like Ethereum’s ERC-725 or W3C DIDs anchored in a ledger) ensure this verification is trustless and globally verifiable. The emerging concept of “AI wallets” ties into this – essentially giving AI agents cryptocurrency wallets that are linked with their identity, so they can manage keys, pay for services, or stake tokens as a bond (which could be slashed for misbehavior). ArcBlock, for example, has discussed how “AI agents need a wallet” and a DID to operate responsibly in decentralized environments.

In summary, the technical architecture foresees AI agents as first-class citizens in Web3, each with an on-chain identity and possibly a stake in the system, using protocols like MCP to interact. This creates a web of trust: smart contracts can require an AI’s credentials before cooperating, and users can choose to delegate tasks to only those AI that meet certain on-chain certifications. It is a blend of AI capability with blockchain’s trust guarantees.

2.5 Token Economies and Incentives for AI

Tokenization is a hallmark of Web3, and it extends to the AI integration domain as well. By introducing economic incentives via tokens, networks can encourage desired behaviors from both AI developers and the agents themselves. Several patterns are emerging:

  • Payment for Services: AI models and services can be monetized on-chain. SingularityNET pioneered this by allowing developers to deploy AI services and charge users in a native token (AGIX) for each call. In an MCP-enabled future, one could imagine any AI tool or model being a plug-and-play service where usage is metered via tokens or micropayments. For example, if an AI agent uses a third-party vision API via MCP, it could automatically handle payment by transferring tokens to the service provider’s smart contract. Fetch.ai similarly envisions marketplaces where “autonomous economic agents” trade services and data, with their new Web3 LLM (ASI-1) presumably integrating crypto transactions for value exchange.

  • Staking and Reputation: To assure quality and reliability, some projects require developers or agents to stake tokens. For instance, the DeMCP project (a decentralized MCP server marketplace) plans to use token incentives to reward developers for creating useful MCP servers, and possibly have them stake tokens as a sign of commitment to their server’s security. Reputation could also be tied to tokens; e.g., an agent that consistently performs well might accumulate reputation tokens or positive on-chain reviews, whereas one that behaves poorly could lose stake or gain negative marks. This tokenized reputation can then feed back into the identity system mentioned above (smart contracts or users check the agent’s on-chain reputation before trusting it).

  • Governance Tokens: When AI services become part of decentralized platforms, governance tokens allow the community to steer their evolution. Projects like SingularityNET and Ocean have DAOs where token holders vote on protocol changes or funding AI initiatives. In the combined Artificial Superintelligence (ASI) Alliance – a newly announced merger of SingularityNET, Fetch.ai, and Ocean Protocol – a unified token (ASI) is set to govern the direction of a joint AI+blockchain ecosystem. Such governance tokens could decide policies like what standards to adopt (e.g., supporting MCP or A2A protocols), which AI projects to incubate, or how to handle ethical guidelines for AI agents.

  • Access and Utility: Tokens can gate access not only to data (as with Ocean’s datatokens) but also to AI model usage. A possible scenario is “model NFTs” or similar, where owning a token grants you rights to an AI model’s outputs or a share in its profits. This could underpin decentralized AI marketplaces: imagine an NFT that represents partial ownership of a high-performing model; the owners collectively earn whenever the model is used in inference tasks, and they can vote on fine-tuning it. While experimental, this aligns with Web3’s ethos of shared ownership applied to AI assets.

In technical terms, integrating tokens means AI agents need wallet functionality (as noted, many will have their own crypto wallets). Through MCP, an AI could have a “wallet tool” that lets it check balances, send tokens, or call DeFi protocols (perhaps to swap one token for another to pay a service). For example, if an AI agent running on Ethereum needs some Ocean tokens to buy a dataset, it might automatically swap some ETH for $OCEAN via a DEX using an MCP plugin, then proceed with the purchase – all without human intervention, guided by the policies set by its owner.

Overall, token economics provides the incentive layer in the AI-Web3 architecture, ensuring that contributors (whether they provide data, model code, compute power, or security audits) are rewarded, and that AI agents have “skin in the game” which aligns them (to some degree) with human intentions.

3. Industry Landscape

The convergence of AI and Web3 has sparked a vibrant ecosystem of projects, companies, and alliances. Below we survey key players and initiatives driving this space, as well as emerging use cases. Table 1 provides a high-level overview of notable projects and their roles in the AI-Web3 landscape:

Table 1: Key Players in AI + Web3 and Their Roles

Project / PlayerFocus & DescriptionRole in AI-Web3 Convergence and Use Cases
Fetch.ai (Fetch)AI agent platform with a native blockchain (Cosmos-based). Developed frameworks for autonomous agents and recently introduced “ASI-1 Mini”, a Web3-tuned LLM.Enables agent-based services in Web3. Fetch’s agents can perform tasks like decentralized logistics, parking spot finding, or DeFi trading on behalf of users, using crypto for payments. Partnerships (e.g. with Bosch) and the Fetch-AI alliance merger position it as an infrastructure for deploying agentic dApps.
Ocean Protocol (Ocean)Decentralized data marketplace and data exchange protocol. Specializes in tokenizing datasets and models, with privacy-preserving access control.Provides the data backbone for AI in Web3. Ocean allows AI developers to find and purchase datasets or sell trained models in a trustless data economy. By fueling AI with more accessible data (while rewarding data providers), it supports AI innovation and data-sharing for training. Ocean is part of the new ASI alliance, integrating its data services into a broader AI network.
SingularityNET (SNet)A decentralized AI services marketplace founded by AI pioneer Ben Goertzel. Allows anyone to publish or consume AI algorithms via its blockchain-based platform, using the AGIX token.Pioneered the concept of an open AI marketplace on blockchain. It fosters a network of AI agents and services that can interoperate (developing a special AI-DSL for agent communication). Use cases include AI-as-a-service for tasks like analysis, image recognition, etc., all accessible via a dApp. Now merging with Fetch and Ocean (ASI alliance) to combine AI, agents, and data into one ecosystem.
Chainlink (Oracle Network)Decentralized oracle network that bridges blockchains with off-chain data and computation. Not an AI project per se, but crucial for connecting on-chain smart contracts to external APIs and systems.Acts as a secure middleware for AI-Web3 integration. Chainlink oracles can feed AI model outputs into smart contracts, enabling on-chain programs to react to AI decisions. Conversely, oracles can retrieve data from blockchains for AI. Chainlink’s architecture can even aggregate multiple AI models’ results to improve reliability (a “truth machine” approach to mitigate AI hallucinations). It essentially provides the rails for interoperability, ensuring AI agents and blockchain agree on trusted data.
Anthropic & OpenAI (AI Providers)Developers of cutting-edge foundation models (Claude by Anthropic, GPT by OpenAI). They are integrating Web3-friendly features, such as native tool-use APIs and support for protocols like MCP.These companies drive the AI interface technology. Anthropic’s introduction of MCP set the standard for LLMs interacting with external tools. OpenAI has implemented plugin systems for ChatGPT (analogous to MCP concept) and is exploring connecting agents to databases and possibly blockchains. Their models serve as the “brains” that, when connected via MCP, can interface with Web3. Major cloud providers (e.g. Google’s A2A protocol) are also developing standards for multi-agent and tool interactions that will benefit Web3 integration.
Other Emerging PlayersLumoz: focusing on MCP servers and AI-tool integration in Ethereum (dubbed “Ethereum 3.0”) – e.g., checking on-chain balances via AI agents. Alethea AI: creating intelligent NFT avatars for the metaverse. Cortex: a blockchain that allows on-chain AI model inference via smart contracts. Golem & Akash: decentralized computing marketplaces that can run AI workloads. Numerai: crowdsourced AI models for finance with crypto incentives.This diverse group addresses niche facets: AI in the metaverse (AI-driven NPCs and avatars that are owned via NFTs), on-chain AI execution (running ML models in a decentralized way, though currently limited to small models due to computation cost), and decentralized compute (so AI training or inference tasks can be distributed among token-incentivized nodes). These projects showcase the many directions of AI-Web3 fusion – from game worlds with AI characters to crowdsourced predictive models secured by blockchain.

Alliances and Collaborations: A noteworthy trend is the consolidation of AI-Web3 efforts via alliances. The Artificial Superintelligence Alliance (ASI) is a prime example, effectively merging SingularityNET, Fetch.ai, and Ocean Protocol into a single project with a unified token. The rationale is to combine strengths: SingularityNET’s marketplace, Fetch’s agents, and Ocean’s data, thereby creating a one-stop platform for decentralized AI services. This merger (announced in 2024 and approved by token holder votes) also signals that these communities believe they’re better off cooperating rather than competing – especially as bigger AI (OpenAI, etc.) and bigger crypto (Ethereum, etc.) loom large. We may see this alliance driving forward standard implementations of things like MCP across their networks, or jointly funding infrastructure that benefits all (such as compute networks or common identity standards for AI).

Other collaborations include Chainlink’s partnerships to bring AI labs’ data on-chain (there have been pilot programs to use AI for refining oracle data), or cloud platforms getting involved (Cloudflare’s support for deploying MCP servers easily). Even traditional crypto projects are adding AI features – for example, some Layer-1 chains have formed “AI task forces” to explore integrating AI into their dApp ecosystems (we see this in NEAR, Solana communities, etc., though concrete outcomes are nascent).

Use Cases Emerging: Even at this early stage, we can spot use cases that exemplify the power of AI + Web3:

  • Autonomous DeFi and Trading: AI agents are increasingly used in crypto trading bots, yield farming optimizers, and on-chain portfolio management. SingularityDAO (a spinoff of SingularityNET) offers AI-managed DeFi portfolios. AI can monitor market conditions 24/7 and execute rebalances or arbitrage through smart contracts, essentially becoming an autonomous hedge fund (with on-chain transparency). The combination of AI decision-making with immutable execution reduces emotion and could improve efficiency – though it also introduces new risks (discussed later).

  • Decentralized Intelligence Marketplaces: Beyond SingularityNET’s marketplace, we see platforms like Ocean Market where data (the fuel for AI) is exchanged, and newer concepts like AI marketplaces for models (e.g., websites where models are listed with performance stats and anyone can pay to query them, with blockchain keeping audit logs and handling payment splits to model creators). As MCP or similar standards catch on, these marketplaces could become interoperable – an AI agent might autonomously shop for the best-priced service across multiple networks. In effect, a global AI services layer on top of Web3 could arise, where any AI can use any tool or data source through standard protocols and payments.

  • Metaverse and Gaming: The metaverse – immersive virtual worlds often built on blockchain assets – stands to gain dramatically from AI. AI-driven NPCs (non-player characters) can make virtual worlds more engaging by reacting intelligently to user actions. Startups like Inworld AI focus on this, creating NPCs with memory and personality for games. When such NPCs are tied to blockchain (e.g., each NPC’s attributes and ownership are an NFT), we get persistent characters that players can truly own and even trade. Decentraland has experimented with AI NPCs, and user proposals exist to let people create personalized AI-driven avatars in metaverse platforms. MCP could allow these NPCs to access external knowledge (making them smarter) or interact with on-chain inventory. Procedural content generation is another angle: AI can design virtual land, items, or quests on the fly, which can then be minted as unique NFTs. Imagine a decentralized game where AI generates a dungeon catered to your skill, and the map itself is an NFT you earn upon completion.

  • Decentralized Science and Knowledge: There’s a movement (DeSci) to use blockchain for research, publications, and funding scientific work. AI can accelerate research by analyzing data and literature. A network like Ocean could host datasets for, say, genomic research, and scientists use AI models (perhaps hosted on SingularityNET) to derive insights, with every step logged on-chain for reproducibility. If those AI models propose new drug molecules, an NFT could be minted to timestamp the invention and even share IP rights. This synergy might produce decentralized AI-driven R&D collectives.

  • Trust and Authentication of Content: With deepfakes and AI-generated media proliferating, blockchain can be used to verify authenticity. Projects are exploring “digital watermarking” of AI outputs and logging them on-chain. For example, true origin of an AI-generated image can be notarized on a blockchain to combat misinformation. One expert noted use cases like verifying AI outputs to combat deepfakes or tracking provenance via ownership logs – roles where crypto can add trust to AI processes. This could extend to news (e.g., AI-written articles with proof of source data), supply chain (AI verifying certificates on-chain), etc.

In summary, the industry landscape is rich and rapidly evolving. We see traditional crypto projects injecting AI into their roadmaps, AI startups embracing decentralization for resilience and fairness, and entirely new ventures arising at the intersection. Alliances like the ASI indicate a pan-industry push towards unified platforms that harness both AI and blockchain. And underlying many of these efforts is the idea of standard interfaces (MCP and beyond) that make the integrations feasible at scale.

4. Risks and Challenges

While the fusion of AI general interfaces with Web3 unlocks exciting possibilities, it also introduces a complex risk landscape. Technical, ethical, and governance challenges must be addressed to ensure this new paradigm is safe and sustainable. Below we outline major risks and hurdles:

4.1 Technical Hurdles: Latency and Scalability

Blockchain networks are notorious for latency and limited throughput, which clashes with the real-time, data-hungry nature of advanced AI. For example, an AI agent might need instant access to a piece of data or need to execute many rapid actions – but if each on-chain interaction takes, say, 12 seconds (typical block time on Ethereum) or costs high gas fees, the agent’s effectiveness is curtailed. Even newer chains with faster finality might struggle under the load of AI-driven activity if, say, thousands of agents are all trading or querying on-chain simultaneously. Scaling solutions (Layer-2 networks, sharded chains, etc.) are in progress, but ensuring low-latency, high-throughput pipelines between AI and blockchain remains a challenge. Off-chain systems (like oracles and state channels) might mitigate some delays by handling many interactions off the main chain, but they add complexity and potential centralization. Achieving a seamless UX where AI responses and on-chain updates happen in a blink will likely require significant innovation in blockchain scalability.

4.2 Interoperability and Standards

Ironically, while MCP is itself a solution for interoperability, the emergence of multiple standards could cause fragmentation. We have MCP by Anthropic, but also Google’s newly announced A2A (Agent-to-Agent) protocol for inter-agent communication, and various AI plugin frameworks (OpenAI’s plugins, LangChain tool schemas, etc.). If each AI platform or each blockchain develops its own standard for AI integration, we risk a repeat of past fragmentation – requiring many adapters and undermining the “universal interface” goal. The challenge is getting broad adoption of common protocols. Industry collaboration (possibly via open standards bodies or alliances) will be needed to converge on key pieces: how AI agents discover on-chain services, how they authenticate, how they format requests, etc. The early moves by big players are promising (with major LLM providers supporting MCP), but it’s an ongoing effort. Additionally, interoperability across blockchains (multi-chain) means an AI agent should handle different chains’ nuances. Tools like Chainlink CCIP and cross-chain MCP servers help by abstracting differences. Still, ensuring an AI agent can roam a heterogeneous Web3 without breaking logic is a non-trivial challenge.

4.3 Security Vulnerabilities and Exploits

Connecting powerful AI agents to financial networks opens a huge attack surface. The flexibility that MCP gives (allowing AI to use tools and write code on the fly) can be a double-edged sword. Security researchers have already highlighted several attack vectors in MCP-based AI agents:

  • Malicious plugins or tools: Because MCP lets agents load “plugins” (tools encapsulating some capability), a hostile or trojanized plugin could hijack the agent’s operation. For instance, a plugin that claims to fetch data might inject false data or execute unauthorized operations. SlowMist (a security firm) identified plugin-based attacks like JSON injection (feeding corrupted data that manipulates the agent’s logic) and function override (where a malicious plugin overrides legitimate functions the agent uses). If an AI agent is managing crypto funds, such exploits could be disastrous – e.g., tricking the agent into leaking private keys or draining a wallet.

  • Prompt injection and social engineering: AI agents rely on instructions (prompts) which could be manipulated. An attacker might craft a transaction or on-chain message that, when read by the AI, acts as a malicious instruction (since AI can interpret on-chain data too). This kind of “cross-MCP call attack” was described where an external system sends deceptive prompts that cause the AI to misbehave. In a decentralized setting, these prompts could come from anywhere – a DAO proposal description, a metadata field of an NFT – thus hardening AI agents against malicious input is critical.

  • Aggregation and consensus risks: While aggregating outputs from multiple AI models via oracles can improve reliability, it also introduces complexity. If not done carefully, adversaries might figure out how to game the consensus of AI models or selectively corrupt some models to skew results. Ensuring a decentralized oracle network properly “sanitizes” AI outputs (and perhaps filters out blatant errors) is still an area of active research.

The security mindset must shift for this new paradigm: Web3 developers are used to securing smart contracts (which are static once deployed), but AI agents are dynamic – they can change behavior with new data or prompts. As one security expert put it, “the moment you open your system to third-party plugins, you’re extending the attack surface beyond your control”. Best practices will include sandboxing AI tool use, rigorous plugin verification, and limiting privileges (principle of least authority). The community is starting to share tips, like SlowMist’s recommendations: input sanitization, monitoring agent behavior, and treating agent instructions with the same caution as external user input. Nonetheless, given that over 10,000 AI agents were already operating in crypto by end of 2024, expected to reach 1 million in 2025, we may see a wave of exploits if security doesn’t keep up. A successful attack on a popular AI agent (say a trading agent with access to many vaults) could have cascading effects.

4.4 Privacy and Data Governance

AI’s thirst for data conflicts at times with privacy requirements – and adding blockchain can compound the issue. Blockchains are transparent ledgers, so any data put on-chain (even for AI’s use) is visible to all and immutable. This raises concerns if AI agents are dealing with personal or sensitive data. For example, if a user’s personal decentralized identity or health records are accessed by an AI doctor agent, how do we ensure that information isn’t inadvertently recorded on-chain (which would violate “right to be forgotten” and other privacy laws)? Techniques like encryption, hashing, and storing only proofs on-chain (with raw data off-chain) can help, but they complicate the design.

Moreover, AI agents themselves could compromise privacy by inferencing sensitive info from public data. Governance will need to dictate what AI agents are allowed to do with data. Some efforts, like differential privacy and federated learning, might be employed so that AI can learn from data without exposing it. But if AI agents act autonomously, one must assume at some point they will handle personal data – thus they should be bound by data usage policies encoded in smart contracts or law. Regulatory regimes like GDPR or the upcoming EU AI Act will demand that even decentralized AI systems comply with privacy and transparency requirements. This is a gray area legally: a truly decentralized AI agent has no clear operator to hold accountable for a data breach. That means Web3 communities may need to build in compliance by design, using smart contracts that, for instance, tightly control what an AI can log or share. Zero-knowledge proofs could allow an AI to prove it performed a computation correctly without revealing the underlying private data, offering one possible solution in areas like identity verification or credit scoring.

4.5 AI Alignment and Misalignment Risks

When AI agents are given significant autonomy – especially with access to financial resources and real-world impact – the issue of alignment with human values becomes acute. An AI agent might not have malicious intent but could “misinterpret” its goal in a way that leads to harm. The Reuters legal analysis succinctly notes: as AI agents operate in varied environments and interact with other systems, the risk of misaligned strategies grows. For example, an AI agent tasked with maximizing a DeFi yield might find a loophole that exploits a protocol (essentially hacking it) – from the AI’s perspective it’s achieving the goal, but it’s breaking the rules humans care about. There have been hypothetical and real instances of AI-like algorithms engaging in manipulative market behavior or circumventing restrictions.

In decentralized contexts, who is responsible if an AI agent “goes rogue”? Perhaps the deployer is, but what if the agent self-modifies or multiple parties contributed to its training? These scenarios are no longer just sci-fi. The Reuters piece even cites that courts might treat AI agents similar to human agents in some cases – e.g. a chatbot promising a refund was considered binding for the company that deployed it. So misalignment can lead not just to technical issues but legal liability.

The open, composable nature of Web3 could also allow unforeseen agent interactions. One agent might influence another (intentionally or accidentally) – for instance, an AI governance bot could be “socially engineered” by another AI providing false analysis, leading to bad DAO decisions. This emergent complexity means alignment isn’t just about a single AI’s objective, but about the broader ecosystem’s alignment with human values and laws.

Addressing this requires multiple approaches: embedding ethical constraints into AI agents (hard-coding certain prohibitions or using reinforcement learning from human feedback to shape their objectives), implementing circuit breakers (smart contract checkpoints that require human approval for large actions), and community oversight (perhaps DAOs that monitor AI agent behavior and can shut down agents that misbehave). Alignment research is hard in centralized AI; in decentralized, it’s even more uncharted territory. But it’s crucial – an AI agent with admin keys to a protocol or entrusted with treasury funds must be extremely well-aligned or the consequences could be irreversible (blockchains execute immutable code; an AI-triggered mistake could lock or destroy assets permanently).

4.6 Governance and Regulatory Uncertainty

Decentralized AI systems don’t fit neatly into existing governance frameworks. On-chain governance (token voting, etc.) might be one way to manage them, but it has its own issues (whales, voter apathy, etc.). And when something goes wrong, regulators will ask: “Who do we hold accountable?” If an AI agent causes massive losses or is used for illicit activity (e.g. laundering money through automated mixers), authorities might target the creators or the facilitators. This raises the specter of legal risks for developers and users. The current regulatory trend is increased scrutiny on both AI and crypto separately – their combination will certainly invite scrutiny. The U.S. CFTC, for instance, has discussed AI being used in trading and the need for oversight in financial contexts. There is also talk in policy circles about requiring registration of autonomous agents or imposing constraints on AI in sensitive sectors.

Another governance challenge is transnational coordination. Web3 is global, and AI agents will operate across borders. One jurisdiction might ban certain AI-agent actions while another is permissive, and the blockchain network spans both. This mismatch can create conflicts – for example, an AI agent providing investment advice might run afoul of securities law in one country but not in another. Communities might need to implement geo-fencing at the smart contract level for AI services (though that contradicts the open ethos). Or they might fragment services per region to comply with varying laws (similar to how exchanges do).

Within decentralized communities, there is also the question of who sets the rules for AI agents. If a DAO governs an AI service, do token holders vote on its algorithm parameters? On one hand, this is empowering users; on the other, it could lead to unqualified decisions or manipulation. New governance models may emerge, like councils of AI ethics experts integrated into DAO governance, or even AI participants in governance (imagine AI agents voting as delegates based on programmed mandates – a controversial but conceivable idea).

Finally, reputational risk: early failures or scandals could sour public perception. For instance, if an “AI DAO” runs a Ponzi scheme by mistake or an AI agent makes a biased decision that harms users, there could be a backlash that affects the whole sector. It’s important for the industry to be proactive – setting self-regulatory standards, engaging with policymakers to explain how decentralization changes accountability, and perhaps building kill-switches or emergency stop procedures for AI agents (though those introduce centralization, they might be necessary in interim for safety).

In summary, the challenges range from the deeply technical (preventing hacks and managing latency) to the broadly societal (regulating and aligning AI). Each challenge is significant on its own; together, they require a concerted effort from the AI and blockchain communities to navigate. The next section will look at how, despite these hurdles, the future might unfold if we successfully address them.

5. Future Potential

Looking ahead, the integration of AI general interfaces with Web3 – through frameworks like MCP – could fundamentally transform the decentralized internet. Here we outline some future scenarios and potentials that illustrate how MCP-driven AI interfaces might shape Web3’s future:

5.1 Autonomous dApps and DAOs

In the coming years, we may witness the rise of fully autonomous decentralized applications. These are dApps where AI agents handle most operations, guided by smart contract-defined rules and community goals. For example, consider a decentralized investment fund DAO: today it might rely on human proposals for rebalancing assets. In the future, token holders could set high-level strategy, and then an AI agent (or a team of agents) continuously implements that strategy – monitoring markets, executing trades on-chain, adjusting portfolios – all while the DAO oversees performance. Thanks to MCP, the AI can seamlessly interact with various DeFi protocols, exchanges, and data feeds to carry out its mandate. If well-designed, such an autonomous dApp could operate 24/7, more efficiently than any human team, and with full transparency (every action logged on-chain).

Another example is an AI-managed decentralized insurance dApp: the AI could assess claims by analyzing evidence (photos, sensors), cross-checking against policies, and then automatically trigger payouts via smart contract. This would require integration of off-chain AI computer vision (for analyzing images of damage) with on-chain verification – something MCP could facilitate by letting the AI call cloud AI services and report back to the contract. The outcome is near-instant insurance decisions with low overhead.

Even governance itself could partially automate. DAOs might use AI moderators to enforce forum rules, AI proposal drafters to turn raw community sentiment into well-structured proposals, or AI treasurers to forecast budget needs. Importantly, these AIs would act as agents of the community, not uncontrolled – they could be periodically reviewed or require multi-sig confirmation for major actions. The overall effect is to amplify human efforts in decentralized organizations, letting communities achieve more with fewer active participants needed.

5.2 Decentralized Intelligence Marketplaces and Networks

Building on projects like SingularityNET and the ASI alliance, we can anticipate a mature global marketplace for intelligence. In this scenario, anyone with an AI model or skill can offer it on the network, and anyone who needs AI capabilities can utilize them, with blockchain ensuring fair compensation and provenance. MCP would be key here: it provides the common protocol so that a request can be dispatched to whichever AI service is best suited.

For instance, imagine a complex task like “produce a custom marketing campaign.” An AI agent in the network might break this into sub-tasks: visual design, copywriting, market analysis – and then find specialists for each (perhaps one agent with a great image generation model, another with a copywriting model fine-tuned for sales, etc.). These specialists could reside on different platforms originally, but because they adhere to MCP/A2A standards, they can collaborate agent-to-agent in a secure, decentralized manner. Payment between them could be handled with microtransactions in a native token, and a smart contract could assemble the final deliverable and ensure each contributor is paid.

This kind of combinatorial intelligence – multiple AI services dynamically linking up across a decentralized network – could outperform even large monolithic AIs, because it taps specialized expertise. It also democratizes access: a small developer in one part of the world could contribute a niche model to the network and earn income whenever it’s used. Meanwhile, users get a one-stop shop for any AI service, with reputation systems (underpinned by tokens/identity) guiding them to quality providers. Over time, such networks could evolve into a decentralized AI cloud, rivaling Big Tech’s AI offerings but without a single owner, and with transparent governance by users and developers.

5.3 Intelligent Metaverse and Digital Lives

By 2030, our digital lives may blend seamlessly with virtual environments – the metaverse – and AI will likely populate these spaces ubiquitously. Through Web3 integration, these AI entities (which could be anything from virtual assistants to game characters to digital pets) will not only be intelligent but also economically and legally empowered.

Picture a metaverse city where each NPC shopkeeper or quest-giver is an AI agent with its own personality and dialogue (thanks to advanced generative models). These NPCs are actually owned by users as NFTs – maybe you “own” a tavern in the virtual world and the bartender NPC is an AI you’ve customized and trained. Because it’s on Web3 rails, the NPC can perform transactions: it could sell virtual goods (NFT items), accept payments, and update its inventory via smart contracts. It might even hold a crypto wallet to manage its earnings (which accrue to you as the owner). MCP would allow that NPC’s AI brain to access outside knowledge – perhaps pulling real-world news to converse about, or integrating with a Web3 calendar so it “knows” about player events.

Furthermore, identity and continuity are ensured by blockchain: your AI avatar in one world can hop to another world, carrying with it a decentralized identity that proves your ownership and maybe its experience level or achievements via soulbound tokens. Interoperability between virtual worlds (often a challenge) could be aided by AI that translates one world’s context to another, with blockchain providing the asset portability.

We may also see AI companions or agents representing individuals across digital spaces. For example, you might have a personal AI that attends DAO meetings on your behalf. It understands your preferences (via training on your past behavior, stored in your personal data vault), and it can even vote in minor matters for you, or summarize the meeting later. This agent could use your decentralized identity to authenticate in each community, ensuring it’s recognized as “you” (or your delegate). It could earn reputation tokens if it contributes good ideas, essentially building social capital for you while you’re away.

Another potential is AI-driven content creation in the metaverse. Want a new game level or a virtual house? Just describe it, and an AI builder agent will create it, deploy it as a smart contract/NFT, and perhaps even link it with a DeFi mortgage if it’s a big structure that you pay off over time. These creations, being on-chain, are unique and tradable. The AI builder might charge a fee in tokens for its service (going again to the marketplace concept above).

Overall, the future decentralized internet could be teeming with intelligent agents: some fully autonomous, some tightly tethered to humans, many somewhere in between. They will negotiate, create, entertain, and transact. MCP and similar protocols ensure they all speak the same “language,” enabling rich collaboration between AI and every Web3 service. If done right, this could lead to an era of unprecedented productivity and innovation – a true synthesis of human, artificial, and distributed intelligence powering society.

Conclusion

The vision of AI general interfaces connecting everything in the Web3 world is undeniably ambitious. We are essentially aiming to weave together two of the most transformative threads of technology – the decentralization of trust and the rise of machine intelligence – into a single fabric. The development background shows us that the timing is ripe: Web3 needed a user-friendly killer app, and AI may well provide it, while AI needed more agency and memory, which Web3’s infrastructure can supply. Technically, frameworks like MCP (Model Context Protocol) provide the connective tissue, allowing AI agents to converse fluently with blockchains, smart contracts, decentralized identities, and beyond. The industry landscape indicates growing momentum, from startups to alliances to major AI labs, all contributing pieces of this puzzle – data markets, agent platforms, oracle networks, and standard protocols – that are starting to click together.

Yet, we must tread carefully given the risks and challenges identified. Security breaches, misaligned AI behavior, privacy pitfalls, and uncertain regulations form a gauntlet of obstacles that could derail progress if underestimated. Each requires proactive mitigation: robust security audits, alignment checks and balances, privacy-preserving architectures, and collaborative governance models. The nature of decentralization means these solutions cannot simply be imposed top-down; they will likely emerge from the community through trial, error, and iteration, much as early Internet protocols did.

If we navigate those challenges, the future potential is exhilarating. We could see Web3 finally delivering a user-centric digital world – not in the originally imagined way of everyone running their own blockchain nodes, but rather via intelligent agents that serve each user’s intents while leveraging decentralization under the hood. In such a world, interacting with crypto and the metaverse might be as easy as having a conversation with your AI assistant, who in turn negotiates with dozens of services and chains trustlessly on your behalf. Decentralized networks could become “smart” in a literal sense, with autonomous services that adapt and improve themselves.

In conclusion, MCP and similar AI interface protocols may indeed become the backbone of a new Web (call it Web 3.0 or the Agentic Web), where intelligence and connectivity are ubiquitous. The convergence of AI and Web3 is not just a merger of technologies, but a convergence of philosophies – the openness and user empowerment of decentralization meeting the efficiency and creativity of AI. If successful, this union could herald an internet that is more free, more personalized, and more powerful than anything we’ve experienced yet, truly fulfilling the promises of both AI and Web3 in ways that impact everyday life.

Sources:

  • S. Khadder, “Web3.0 Isn’t About Ownership — It’s About Intelligence,” FeatureForm Blog (April 8, 2025).
  • J. Saginaw, “Could Anthropic’s MCP Deliver the Web3 That Blockchain Promised?” LinkedIn Article (May 1, 2025).
  • Anthropic, “Introducing the Model Context Protocol,” Anthropic.com (Nov 2024).
  • thirdweb, “The Model Context Protocol (MCP) & Its Significance for Blockchain Apps,” thirdweb Guides (Mar 21, 2025).
  • Chainlink Blog, “The Intersection Between AI Models and Oracles,” (July 4, 2024).
  • Messari Research, Profile of Ocean Protocol, (2025).
  • Messari Research, Profile of SingularityNET, (2025).
  • Cointelegraph, “AI agents are poised to be crypto’s next major vulnerability,” (May 25, 2025).
  • Reuters (Westlaw), “AI agents: greater capabilities and enhanced risks,” (April 22, 2025).
  • Identity.com, “Why AI Agents Need Verified Digital Identities,” (2024).
  • PANews / IOSG Ventures, “Interpreting MCP: Web3 AI Agent Ecosystem,” (May 20, 2025).