How Does MCP Work?
An Open Protocol Connecting AI Models with External Tools in a Standardized Way
MCP (Model Context Protocol) is an open protocol published by Anthropic that standardizes the connection between AI models and external tools/data sources. Previously, integrating AI with DBs, files, APIs, etc. required separate implementations for each (N×M problem), and MCP unifies this into a single standard. The architecture follows a Host (LLM service) → Client (connection management) → Server (external system connection) structure, where the Server provides Prompts (guidelines), Resources (reference data), and Tools (executable functions), while the Client provides Roots (file system access) and Sampling (requesting AI help). Various SDKs are available for TypeScript, Python, and more, with over 4,000 MCP servers registered on hubs like Smithery.
Architecture Diagram
- Host: LLM service like Claude (user interface)
- Client: Manages connections to MCP Server inside Host
- Server: Independent process that exposes external systems (DB, files, API) to AI
- Communication uses JSON-RPC based, transport options: stdio/SSE/HTTP
- Over 4,000+ MCP servers registered on hubs like Smithery
How It Works
Host (Claude and other LLM services) initializes MCP Client
Client requests connection to MCP Server (stdio, SSE, HTTP and other transports)
Server delivers list of available Tools, Resources, and Prompts to Client
AI model analyzes user request and determines necessary Tool calls
Client executes Server's Tool (DB query, file read, API call, etc.)
Server returns execution result to Client → AI model uses result to generate response
Pros
- ✓ Solves N×M integration problem (standard protocol)
- ✓ Tool developers and LLM developers can develop independently
- ✓ Open source, available to everyone
- ✓ Various language SDKs available (TypeScript, Python, etc.)
- ✓ Systematic structure with Tools/Resources/Prompts
Cons
- ✗ Still early stage (ecosystem growing)
- ✗ Server implementation and security management needed
- ✗ Network dependency (tools unavailable when Server is down)
- ✗ Varying MCP support levels across AI models