From as an experimental side project on anthropic, Model Context Protocol (MCP) has become the de facto standard for orchestral interior interactions across data sets, calculation resources and external artifacts.
It can represent one of the most transformative protocols for the AI era and a good fit for web3 architectures.
Like HTTP revolutionary web communication, MCP provides a universal framework that supports virtually all major AI platform’s ability to integrate smart agents with different sources of information and operational end points.
A brief introduction to MCP
MCP was originally designed to streamline interactions between prototype agents and document stores. Early success in coordinating retrieval and reasoning workflows caught other laboratories, and by mid -2024, scientists had rolled out open source reference implementations.
A wave of community-driven extensions soon followed, enabling MCP to support safe identification exchange, federated learning scenarios and plugin-style resource adapters. At the beginning of 2025, leading platform-inclusive Openai, Google Deepmind and Meta AI had MCP naturally and cemented its role as HTTP-equivalent protocol for agent communication.
MCP uses a light client -server -paradigm with three main participants: MCP host (an AI application orchestrating requests), one or more MCP clients (components that maintain dedicated connections) and MCP servers (services revealing contextual primitives). Each client -server pair communicates over a distinct channel, enabling parallel context sourcing from multiple servers.
MCP’s data layer is about three basic primitives – tools, resources and requests – that together authorize seamless agent cooperation.
Tools encapsulate remote operations or features that an agent can invoke to perform specialized tasks, while resources represent the data’s endpoints such as databases, vector stores and on-chain-oracles from which agents can retrieve contextual information.
Prompt serves as structured templates that control an agent’s reasoning process defines how input should be formulated and interpreted. By standardizing these core building blocks, MCP ensures that different agents can detect, request and utilize capabilities in a consistent, interoperable way across any underlying infrastructure.
MCP and Web3
From a first-principle point of view, the intersection of web3 and MCP could realize in two key areas:
- Activating each blockchain -Dataset and decentralized protocol to act as a mcp server or client
- Use web3 to run a new generation of MCP networks.
Together, these imperatives promise an expandable, conflicting substance of agent for agentic intelligence.
Web3 -Data as mcp -artefacts
To catalyze AI agents in crypto environments, trouble-free access to on-chain data and functionality in the smart contract is of the utmost importance. We see for us blockchain hub points that reveal blockage and transaction history through MCP servers, while DEFI platforms publish composed operations via MCP interfaces.
Complement this pattern, traditional cryptodateways – exchanges, wallets, explorers – ACT such as MCP clients, uniform query and processing of context. Imagine a single agent simultaneously interface with Aave’s lending markets, Layer0’s transverse chain bridges and MEV analysis, through the same coherent programming interface.
Web3 MCP network
MCP is an incredibly powerful protocol, but like HTTP it will develop from isolated final points to operating complete networks. These days, the use of MCP still requires detailed knowledge of client and server endpoints. Similarly, capabilities such as approval and identity are core lack of blocks from the protocols, but important for the streamline adoption of MCP.
The next phase of MCP will be driven by network platforms that enable some more sophisticated options:
- Dynamic discovery that surfaces the right MCP endpoints for a given task.
- Search features that allow agents to find the right MCP endpoints.
- Reviews of MCP servers and clients to pull their reputation.
- Coordination of MCP servers to achieve a specific result.
- Verifiableness of the output produced by MCP points.
- Traceability of interactions with MCP clients and servers
- Authentication and access control mechanisms for MCP servers.
Many of these capabilities require the right level of financial incentives to coordinate the nodes in an MCP network. This seems like a match made in the AI sky to web3. Traceability, confidence -free and verifiable calculations are some of the most important primitives that can operate the first generation of MCP networks. Web3 is the most effective technology for several generations to operate calculation networks and MCP needs new networks.
Project Namda
The idea of combining web3 and MCP to operate a new generation of MCP networks is not theoretical of any stretch and we begin to see real progress in space. One of the most interesting initiatives in this area is Mits Project Namda.
Namda, led by researchers at Csail and Mit-Ibm Watson Ai Lab, was launched by 2024 to pioneer scalable, distributed agent frames built on MCP’s Messaging Funds. Namda (Networked Agency Modular Distributed Architecture) creates an open ecosystem where heterogeneous agents – Spanning Cloud services, Edge Tones and Specialized Accelerators – can seamlessly exchange context and coordinate complex workflows. By utilizing MCP’s standardized JSON-RPC primitives, Namda demonstrates, where large scale, low-laten’s cooperation can be achieved without sacrificing interoperability or security.
Namda’s architecture already contains 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 politics-driven resource arbitration, enabling reliable workflows with multiple parties. Extensions to token-based incentive mechanisms and end-to-end-emergency tracking enrich the additional protocol, with early prototypes illustrating effective federal learning on vision-and-language tasks across global test beds.
Another foundation for decentralized AI
For decades, decentralized AI has struggled to find a clear fit for Power Mainstream AI applications. The emergence of MCP and the need for MCP networks has quickly become one of the most prominent use cases for a new generation of AI infrastructure. This can be one of the largest use cases in AI and one that web3 is perfectly suited to address. The combination of web3 and MCP may be just a new foundation for decentralized AI.
