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MCP in the Web3 Ecosystem: A Comprehensive Review

· 49 min read
Dora Noda
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 Oracles – Connecting 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 MCP – AI 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