From a project on the experimental side at Anthropic, the Model Context Protocol (MCP) has become the de facto standard for orchestrating agent interactions through data sets, IT resources and external artefacts.
It can represent one of the most transformative protocols for the AI era and an excellent adjustment for web 3 architectures.
Like HTTP web communications, MCP provides a universal framework which underlying practically all the main AI platforms to integrate intelligent agents with various sources of information and operational termination points.
A short MCP intro
MCP was initially designed to rationalize interactions between prototype agents and document stores. The early success of the coordination of recovery and reasoning work flows drew the attention of other laboratories and, in mid-2014, the researchers had deployed open source reference implementations.
An increase in extensions focused on the community quickly followed, allowing MCP to support the secure identification exchange, federated learning scenarios and plugin style resources adapters. At the beginning of 2025, the main platforms – including OPENAI, Google Deepmind and Meta AI – had adopted MCP natively, cement its role of HTTP equivalent protocol for agency communications.
MCP uses a light customer-server paradigm with three main participants: MCP host (an AI application for orchestration requests), one or more MCP customers (components retaining dedicated connections) and MCP servers (services exposing contextual primitives). Each client-server pair communicates on a separate channel, allowing a parallel source of context from several servers.
The MCP data layer revolves around three fundamental primitives – Tools, resources and prompts – which are empowering the transparent collaboration of agents together.
Tools encapsulate operations remotely or the functions that an agent can invoke to perform specialized tasks, while resources represent data termination points, such as databases, vector stores and chain oracles – from which agents can recover contextual information.
The prompts serve as structured models guiding the reasoning process of an agent, defining how the entries must be formulated and interpreted. By normalizing these main constituent elements, MCP guarantees that various agents can discover, request and use capacities in a coherent and interoperable way 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:
- Allow each set of blockchain data and each decentralized protocol to operate as a MCP server or customer
- Use web3 to feed a new generation of MCP networks.
Together, these imperatives promise an extensible and minimized fabric for aging intelligence.
Web3 as MCP artefacts
To catalyze AI agents in cryptographic environments, transparent access to chain data and intelligent contract functionality is essential. We are considering blockchain nodes exposing block stories and transactions via MCP servers, while DEFI platforms publish composable operations via MCP interfaces.
Completing this model, traditional cryptographic gateways – exchanges, wallets, explorers – have accompanied as MCP customers, questioning uniformly and treatment. Imagine a single agent interfacing simultaneously with Aave’s loan markets, the transversal layer of layer0 and the MEV analysis, throughout the same coherent programming interface.
MCP web3 networks
MCP is an incredibly powerful protocol but, just like HTTP, it will pass isolated termination points for the supply of complete networks. Nowadays, the use of MCP always requires detailed knowledge of customer and server termination points. Likewise, capacities such as authentication and identity are basic blocks of protocols but essential for the adoption of MCP rationalization.
The next MCP phase will be fed by network platforms that allow more sophisticated capacities:
- Dynamic discovery that surfaces the good MCP termination points for a given task.
- Research capacities that allow agents to find the right MCP termination points.
- The notes of MCP servers and customers to advance their reputation.
- Coordination of MCP servers to obtain a specific result.
- Verification of the outputs produced by the MCP termination points.
- TRACability of interactions with MCP customers and servers
- Authentication and access control mechanisms for MCP servers.
Many of these capacities require the good level of economic incentives to coordinate nodes in an MCP network. It looks like a match made in AI paradise for web3. Traceability, calculations without confidence and verifiable are some of the main primitives that can feed the first generation of MCP networks. Web3 is the most efficient technology of several generations to supply calculation networks and MCP needs new networks.
Namda project
The idea of combining web3 and MCP to fuel a new generation of MCP networks is not theoretical by any section and we are starting to see real progress in space. One of the most interesting initiatives in this field is the MIT Namda project.
Meronné by researchers from CSAIL and Mit-ibM Watson AI LAB, Namda was launched in 2024 in Pioneer Stable and Distributed Agency Frameworks built on MCP messaging foundations. NAMDA (Modular Distributed Architecture Network Agent) creates an open ecosystem where heterogeneous agents – Spanning cloud services, on -board devices and specialized accelerators – can transparently exchange context and coordinate complex work flows. By taking advantage of the primitive JSON-RPC Standardized MCP, Namda shows how much large-scale collaboration can be carried out without sacrificing interoperability or security.
Namda’s architecture already incorporates many ideas from a decentralized MCP network such as the dynamic discovery of nodes, loading of the load and tolerance to the breakdowns through the distributed clusters. With a decentralized register inspired by blockchain techniques, Namda provides identities of verifiable agent and arbitration of the resources focused on policies, allowing multi -party confidence workflows. The extensions of the token incitement mechanisms and the monitoring of the provenance from start to finish still enrich the protocol, with early prototypes illustrating effective federated learning on vision and language tasks in global test beacons.
A different base for decentralized AI
For decades, the decentralized AI had trouble finding a clear adjustment to supply traditional AI applications. The emergence of MCP and the need for MCP networks quickly became one of the most important use cases for a new generation of AI infrastructure. This could be one of the largest cases of using AI and the one that web3 is perfectly suited to address. The web3 and MCP combination may well be a new base for a decentralized AI.
