Starting as an experimental parallel project in Anthrope, the model context protocol (MCP) has become the de facto standard to orchestrate agents interactions in data sets, computer resources and external artifacts.
You can represent one of the most transformative protocols for the AI era and a great option for web3 architectures.
Like HTTP revolutionized web communications, MCP provides a universal framework that practically supports the ability of all the main artificial intelligence platforms to integrate intelligent agents with various sources of information and operating final points.
A brief introduction to MCP
MCP was initially designed to optimize interactions between prototype agents and document stores. Early success in the coordination of recovery and reasoning workflows caught the attention of other laboratories, already mid -2024, researchers had implemented open source reference implementations.
It soon followed an increase in community -driven extensions, allowing MCP to support the exchange of safe credentials, federated learning scenarios and complement -style resource adapters. At the beginning of 2025, the leading platforms, including Openai, Google Deepmind and Meta AI, had adopted MCP natively, consolidating its role as a protocol equivalent to HTTP for agent communications.
MCP uses a client light paradigm -server with three main participants: the Host MCP (an AI application that orchestra applications), one or more MCP customers (components that maintain dedicated connections) and MCP servers (services that exhibit contextual primitive). Each client -server is communicated through a different channel, which allows the supply of parallel context of multiple servers.
The MCP data layer revolves around three fundamental primitive (toolas, resources and indications, which together empower the collaboration of agents without problems.
The tools encapsulate the remote operations or functions that an agent can invoke to execute specialized tasks, while the resources represent the final data points, such as databases, vector stores and chain oracles, from which agents can obtain contextual information.
The indications serve as structured templates that guide the reasoning process of an agent, defining how tickets must be formulated and interpreted. By standardizing these central construction blocks, MCP guarantees that various agents can discover, request and use capacities in a consistent and interoperable manner in any underlying infrastructure.
MCP and web3
From the point of view of the first principles, the intersection of web3 and MCP could materialize in two key areas:
- Enable each blockchain data set and decentralized protocol to operate as an MCP server or customer
- Use Web3 to feed a new generation of MCP networks.
Together, these imperatives promise an extensible and minimized fabric of trust for agent intelligence.
Web3 data such as MCP artifacts
To catalyze AI agents in cryptographic environments, access to data in the chain and intelligent contract functionality is essential. We imagine the blockchain nodes exposed by block records and transactions through MCP servers, while defi platforms publish composite operations through MCP interfaces.
Complementing this pattern, traditional cryptographic link doors (exchanges, wallets, explorers) act as MCP clients, consulting and uniformly processing the context. Imagine a single agent that simultaneously interferes with Aave loan markets, Layer0 cross -chain bridges and MEV analysis, during the same coherent programming interface.
Web3 MCP networks
MCP is an incredibly powerful protocol, but, like HTTP, it will evolve from the isolated final points to complete networks. These days, the use of MCP still requires detailed knowledge of the client’s final and server points. Similarly, capacities such as authentication and identity are blocks that are missing in the core of protocols, but it is essential for the adoption of the MCP current line.
The next MCP phase will be fed by network platforms that allow some more sophisticated capabilities:
- Dynamic discovery that the correct MCP points arise for a specific task.
- Search capabilities that allow agents to find the correct MCP points.
- MCP servers and customer ratings to cover their reputation.
- Coordination of MCP servers to achieve a specific result.
- Verifiability of the outputs produced by the MCP final points.
- The traceability of interactions with customers and MCP servers
- Authentication and access control mechanisms for MCP servers.
Many of these capacities require the correct level of economic incentives to coordinate nodes in an MCP network. This seems like a game made in AI Heaven for Web3. Traceability, calculations without trust and verifiable are some of the key primitive that can feed the first generation of MCP networks. Web3 is the most efficient technology of several generations to turn on computer and MCP networks needs new networks.
NAMDA PROJECT
The idea of combining web3 and MCP to feed a new generation of MCP networks is not theoretical in any period and we are beginning to see real progress in space. One of the most interesting initiatives in this area is the NAMDA of MIT project.
Headed by researchers from Csail and the Mit-Ibm Watson AI laboratory, Namda was launched in 2024 for pioneers in the scalable and distributed agent frames built in the MCP messages bases. NAMDA (distributed modular architecture of the network agent) creates an open ecosystem where heterogeneous agents (cloud services, edge devices and specialized accelerators) can exchange without problems and coordinate complex workflows. By taking advantage of the standardized JSON-RPC MCP, NAMDA demonstrates how collaboration on a large scale and low latency can be achieved without sacrificing interoperability or security.
Namda’s architecture already incorporates many of the ideas of a decentralized MCP network, such as the discovery of dynamic nodes, load balance and tolerance to failures in distributed groups. With a decentralized record inspired by blockchain techniques, NAMDA guarantees identities of verifiable agents and arbitration of resources promoted by policies, allowing workflows of several reliable parts. Extensions for tokens -based incentive mechanisms and the monitoring of extremely to extreme origin further enrich the protocol, with early prototypes that illustrate efficient federated learning in vision and language tasks in global tests.
A different basis for decentralized AI
For decades, decentralized AI has struggled to find a clear adjustment to feed conventional AI applications. The appearance of MCP and the need for MCP networks have quickly become one of the most prominent cases for a new generation of AI infrastructure. This could be one of the greatest cases of use in AI and one that web3 is perfectly suitable to address. The combination of web3 and MCP could be a new basis for the decentralized.
