🧱 MCP Architecture Explained

MCP Architecture

Unlike RESTful services or one-off function calls, MCP deals with stateful model interactions, long-lived sessions, and tool orchestration. To enable this reliably, a structured architecture is critical.

🧱 Key Architectural Layers

1. Client Layer (SDK or App)

  • Sends messages like start_session, execute_tool, end_session
  • Maintains minimal context; delegates memory/state to MCP server

2. MCP Server

  • Core protocol engine

  • Handles:

    • Session lifecycle
    • Context persistence
    • Tool routing & invocation
    • Response marshalling

3. Tool Execution Layer

  • Decoupled execution engine (local or remote)
  • Executes user-defined tools, scripts, API calls, LLM tools, etc.
  • Returns structured results to server

4. Context Store (Memory Layer)

  • Database or vector store

  • Maintains:

    • Current session memory
    • Past interactions for recall
    • Tool registry & config

🔁 Flow Overview (Session + Execution)

sequenceDiagram
    participant User
    participant Client
    participant MCP_Server
    participant Tool_Engine
    participant DB

    User->>Client: StartSession(UserID)
    Client->>MCP_Server: POST /start_session
    MCP_Server->>DB: Create session context
    MCP_Server-->>Client: session_id

    Client->>MCP_Server: POST /execute_tool(tool_id, input, session_id)
    MCP_Server->>Tool_Engine: Execute tool logic
    Tool_Engine-->>MCP_Server: Return result
    MCP_Server->>DB: Save result to memory
    MCP_Server-->>Client: tool result

    Client->>MCP_Server: POST /end_session(session_id)
    MCP_Server->>DB: Archive session
    MCP_Server-->>Client: Session closed

🧩 How Tools Are Managed

  • Tools are registered with unique IDs

  • Each has:

    • Interface schema (input/output)
    • Description for LLM reasoning
    • Endpoint or execution logic

Example:

{
  "tool_id": "get_weather",
  "description": "Returns current weather for a given city",
  "input_schema": { "city": "string" },
  "output_schema": { "temperature": "number", "unit": "string" }
}

⚡ Power of Modularity

The modularity of MCP lets it work across:

  • Local agents
  • Hosted LLM APIs
  • Serverless platforms
  • Edge environments

This architecture allows mixing models, tools, and memory systems while keeping context intact.

📌 Summary

MCP’s architecture allows:

  • Stateful agent workflows
  • Plug-and-play tool execution
  • Seamless memory/context integration
  • Language-agnostic client/server interaction

Up next: MCP in Action: A Developer’s Perspective