mcp.md•3.04 kB
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# Model Context Protocol (MCP)
## What is MCP?
The Model Context Protocol (MCP), open-sourced by Anthropic in late 2024, functions as a **standardized communication layer** between LLMs and external data sources. Often described as the "USB-C port for AI applications," MCP addresses the fundamental challenge of connecting AI models with real-world data and tools.
MCP transforms what was previously an M×N integration problem (requiring custom connections between each AI application and data source) into a simpler M+N problem through standardization. This enables any MCP-compatible AI application to connect with any MCP-compatible data source using a consistent protocol.
## Core Concepts
MCP follows a client-server architecture with three primary primitives:
1. **Tools**: Executable functions that LLMs can call to perform specific actions (similar to POST endpoints in REST APIs). These are model-controlled operations that create side effects.
2. **Resources**: Data sources that LLMs can access to gain context (similar to GET endpoints). These are application-controlled data feeds that provide information.
3. **Prompts**: Pre-defined templates for LLM interactions that are user-controlled, providing standardized ways to use tools and resources.
The protocol supports multiple transport mechanisms:
- **STDIO**: Used for local integrations where the server runs in the same environment as the client
- **HTTP+Streamable**: Used for remote connections with HTTP for client requests and streaming for responses
## DocuMCP Implementation
In our DocuMCP project, we've implemented an MCP server with:
- **STDIO Transport**: Our server uses STDIO for local communication
- **Documentation Tools**: Functions for generating documentation and searching the codebase
- **Code Resources**: Resources for accessing code and documentation
Here's an example of our server initialization:
```typescript
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
// Create server
const server = new Server(
{
name: "Doku-Assistant",
version: "1.0.0",
},
{
capabilities: {
tools: {},
},
},
);
// Register tools and resources...
// Start the server with STDIO transport
const transport = new StdioServerTransport();
await server.connect(transport);
```
## Why MCP Matters for Documentation
For code documentation, MCP provides several advantages:
1. **Standardized Interface**: Consistent way for AI assistants to interact with your codebase
2. **Flexibility**: Support for different LLM providers through a unified protocol
3. **Extensibility**: Easy addition of new tools and resources as needs evolve
4. **Typed Interactions**: Strong typing with Zod schema validation for robust error prevention
By implementing the MCP TypeScript SDK, DocuMCP establishes a foundation for AI-powered documentation generation that can integrate with any MCP-compatible client.