Starting as an experimental side project at Anthropic, the Model Context Protocol (MCP) has become the de facto standard for orchestrating agentic interactions across datasets, computational resources and external artifacts.
It may represent one of the most transformative protocols for the AI era and a great fit for Web3 architectures.
Much like HTTP revolutionized web communications, MCP provides a universal framework that underpins virtually every major AI platform’s ability to integrate smart agents with diverse information sources and operational endpoints.
A Short Intro to MCP
MCP was initially designed to streamline interactions between prototype agents and document stores. Early success in coordinating retrieval and reasoning workflows caught the attention of other labs, and by mid-2024, researchers had rolled out open-source reference implementations.
A surge of community-driven extensions soon followed, enabling MCP to support secure credential exchange, federated learning scenarios, and plugin-style resource adapters. By early 2025, leading platforms—including OpenAI, Google DeepMind, and Meta AI—had adopted MCP natively, cementing its role as the HTTP-equivalent protocol for agentic communications.
MCP employs a lightweight client–server paradigm with three principal participants: the MCP Host (an AI application orchestrating requests), one or more MCP Clients (components maintaining dedicated connections), and MCP Servers (services exposing contextual primitives). Each client–server pair communicates over a distinct channel, enabling parallel context sourcing from multiple servers.
MCP’s Data Layer revolves around three foundational primitives—Tools, Resources, and Prompts—that together empower seamless agent collaboration.
Tools encapsulate remote operations or functions that an agent can invoke to execute specialized tasks, while Resources represent the data endpoints—such as databases, vector stores, and on-chain oracles—from which agents can fetch contextual information.
Prompts serve as structured templates guiding an agent’s reasoning process, defining how inputs should be formulated and interpreted. By standardizing these core building blocks, MCP ensures that diverse agents can discover, request, and utilize capabilities in a consistent, interoperable manner across any underlying infrastructure.
MCP and Web3
From a first-principles standpoint, the intersection of Web3 and MCP could materialize in two key areas:
- Enabling every blockchain dataset and decentralized protocol to operate as an MCP server or client
- Use Web3 to power a new generation of MCP networks.
Together, these imperatives promise an extensible, trust-minimized fabric for agentic intelligence.
Web3 Data as MCP Artifacts
To catalyze AI agents in crypto environments, seamless access to on-chain data and smart-contract functionality is paramount. We envision blockchain nodes exposing block and transaction histories through MCP servers, while DeFi platforms publish composable operations via MCP interfaces.
Complementing this pattern, traditional crypto gateways—exchanges, wallets, explorers—act as MCP clients, uniformly querying and processing context. Imagine a single agent concurrently interfacing with Aave’s lending markets, Layer0’s cross-chain bridges, and MEV analytics, all through the same coherent programming interface.
Web3 MCP Networks
MCP is an incredibly powerful protocol but, just like HTTP, it’s going to evolve from isolated endpoints to powering complete networks. These days, using MCP still requires detailed knowledge of client and server endpoints. Similarly, capabilities such as authentication and identity are core missing blocks from the protocols but essential for the streamline adoption of MCP.
The next phase of MCP is going to be powered by network platforms that enable some more sophisticated capabilities:
- Dynamic discovery that surface the right MCP endpoints for a given task.
- Search capabilities that allow agents find the right MCP endpoints.
- Ratings of MCP servers and clients to tract their reputation.
- Coordination of MCP servers to achieve a specific outcome.
- Verifiability of the outputs produced by MCP endpoints.
- Traceability of the interactions with MCP clients and servers
- Authentication and access control mechanisms for MCP servers.
Many of these capabilities require the right level of economic incentives to coordinate the nodes in an MCP network. This seems like a match made in AI heaven for Web3. Traceability, trustless and verifiable computations are some of the key primitives that can power the first generation of MCP networks. Web3 is the most efficient technology of several generations to power computation networks and MCP needs new networks.
Project Namda
The idea of combining Web3 and MCP to power a new generation of MCP networks is not theoretical by any stretch and we are starting to see real progress in the space. One of the most interesting initiatives in this area is MIT’s Project Namda.
Spearheaded by researchers at CSAIL and the MIT-IBM Watson AI Lab, Namda was launched in 2024 to pioneer scalable, distributed agentic frameworks built on MCP’s messaging foundations. Namda (Networked Agent Modular Distributed Architecture) creates an open ecosystem where heterogeneous agents—spanning cloud services, edge devices, and specialized accelerators—can seamlessly exchange context and coordinate complex workflows. By leveraging MCP’s standardized JSON-RPC primitives, Namda demonstrates how large-scale, low-latency collaboration can be achieved without sacrificing interoperability or security.
Namda’s architecture already incorporates many of the ideas of a decentralized MCP network such as dynamic node discovery, load balancing, and fault tolerance across distributed clusters. With a decentralized registry inspired by blockchain techniques, Namda ensures verifiable agent identities and policy-driven resource arbitration, enabling trusted multi-party workflows. Extensions for token-based incentive mechanisms and end-to-end provenance tracking further enrich the protocol, with early prototypes illustrating efficient federated learning on vision-and-language tasks across global testbeds.
A Different Foundation for Decentralized AI
For decades, decentralized AI has struggled to find a clear fit to power mainstream AI applications. The emergence of MCP and the need for MCP networks have rapidly become one of the most prominent use cases for a new generation of AI infrastructure. This might be one of the biggest use cases in AI and one that Web3 is perfectly suited to address. The combination of Web3 and MCP might just be a new foundation for decentralized AI.
