[2/x] Model Context Protocol (MCP) and Agent-to-Agent (A2A) - MCP architecture (AI column)

Prolonged story I want to talk about how these protocols are technically designed.

MCP architecture It is based on the classical client-server model. The MCP client is an AI application (assistant, agent)An MCP server requires access to external data or functions, and an MCP server is a connector module that can communicate with a specific external service or data repository. MCP formally specifies how servers declare their capabilities (for detection by customers) and how clients request specific actions from servers to produce results. Communication is based on the JSON-RPC protocol. 2.0Messages can be exchanged by different transports: STDIO - Interaction through standard I/O, convenient for local integrations HTTP - REST API with Server-Sent Events support for streaming responses This architecture provides flexibility: the same MCP client (like a chatbot.) You can connect to different MCP servers. (tool) using a single protocol, whether locally or through a network.

In practice, it looks like this: the developer raises the MCP server for some resource – for example, an MCP connector to a database or file storage. The server describes what functions are available. (For example, readFile, queryDatabase) In what format requests/responses are received. AI model (client) MCP can dynamically detect and call available tools without having hard-coded instructions for each integration. The protocol supports two-way exchange: the server can return not only data, but also metadata. (context description, tags) And even generate a sequence of messages to manage complex tasks. To simplify the development of Anthropic released SDK MCP I opened a repository with examples of servers. This means that developers can quickly build their own MCP connectors for any system.

Technically, MCP standardizes three key aspects: 1. Discovery: Clients can ask the server for a list of its capabilities and data schema, obtaining a uniform description of the tools. 2. Invocation: Request format for action and response format (mistake) Strictly defined JSON-RPC, which eliminates ambiguity in communication 3. Context handlingMCP allows you to share contextual information and load data into model hints. For example, an MCP server can not only return raw data, but also provide a model with a built-in hint on how to interpret that data.

It should be noted that MCP is not tied to a specific AI module; it is a level of integration. Any model. (Claude, GPT-4Gemini et al.)Those whose container supports the MCP client will be able to access MCP servers. MCP was quickly picked up by vendors: the standard was originally conceived as open and compatible with all ecosystems.

More information about MCP can be found on websiteThe next post will focus on the A2A protocol architecture.

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