MyAIServ MCP Server

# Model Context Protocol Documentation ## Introduction MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. ## Core Architecture ### Overview MCP follows a client-server architecture where: - **Hosts** are LLM applications (like Claude Desktop or IDEs) that initiate connections - **Clients** maintain 1:1 connections with servers, inside the host application - **Servers** provide context, tools, and prompts to clients ### Key Components 1. **Resources** - File-like data that can be read by clients - Identified by unique URIs - Can contain text or binary data - Application-controlled access 2. **Tools** - Functions that can be called by the LLM - Model-controlled with user approval - Support various operations and integrations - Include input validation and error handling 3. **Prompts** - Pre-written templates for specific tasks - Support dynamic arguments - Include context from resources - Enable workflow automation 4. **Sampling** - Allows servers to request LLM completions - Maintains security and privacy - Supports model preferences - Includes context management ## Transport Layer MCP supports multiple transport mechanisms: 1. **Stdio Transport** - Uses standard input/output for communication - Ideal for local processes - Simple process management 2. **HTTP with SSE Transport** - Server-Sent Events for server-to-client messages - HTTP POST for client-to-server messages - Suitable for network communication ## Security Considerations - Authentication and authorization controls - Input validation and sanitization - Resource access restrictions - Rate limiting and monitoring - Secure transport protocols - Error handling and logging ## Best Practices 1. **Implementation** - Follow SOLID principles - Use strong typing - Implement proper error handling - Add comprehensive logging 2. **Resource Management** - Validate all resource URIs - Implement proper cleanup - Handle concurrent access - Monitor resource usage 3. **Tool Development** - Clear documentation - Input validation - Error handling - Progress reporting 4. **Security** - Validate all inputs - Implement access controls - Monitor usage - Regular updates ## Getting Started 1. **Choose Your Role** - Server Developer: Build custom integrations - Client Developer: Create MCP-enabled applications - User: Utilize existing MCP servers 2. **Setup Environment** - Install required SDKs - Configure development environment - Set up testing tools 3. **Implementation Steps** - Design your integration - Implement core features - Test thoroughly - Deploy and monitor ## SDKs and Tools - Python SDK - TypeScript SDK - MCP Inspector - Development Tools - Testing Utilities ## Community and Support - GitHub Discussions - Documentation - Example Implementations - Best Practices Guides ## Debugging ### Tools Overview 1. **MCP Inspector** - Interactive debugging interface - Direct server testing - Message flow visualization 2. **Claude Desktop Developer Tools** - Integration testing - Log collection - Chrome DevTools integration 3. **Server Logging** - Custom logging implementations - Error tracking - Performance monitoring ### Common Issues 1. **Working Directory** - Use absolute paths in configuration - Consider process start location - Handle path resolution correctly 2. **Environment Variables** - Inherit subset of variables - Configure through server config - Handle sensitive data properly 3. **Server Initialization** - Path resolution - Configuration validation - Environment setup - Permission checks ### Best Debugging Practices 1. **Logging Strategy** - Structured logging - Context inclusion - Error tracking - Performance metrics 2. **Testing Workflow** - Use Inspector for basic testing - Implement integration tests - Monitor logs - Check error handling ## Example Servers ### Official Reference Implementations 1. **Data and File Systems** - Filesystem server - PostgreSQL server - SQLite server - Google Drive integration 2. **Development Tools** - Git server - GitHub integration - GitLab integration - Sentry integration 3. **Web and Browser** - Brave Search - Fetch utilities - Puppeteer automation 4. **Productivity** - Slack integration - Google Maps - Memory management ### Community Servers - Docker management - Kubernetes control - Linear integration - Snowflake database - Spotify control - Todoist management ## Future Development ### Roadmap 1. **Remote MCP Support** - Authentication & Authorization - Service Discovery - Stateless Operations 2. **Reference Implementations** - Client Examples - Protocol Drafting 3. **Distribution & Discovery** - Package Management - Installation Tools - Server Registry 4. **Agent Support** - Hierarchical Agent Systems - Interactive Workflows - Streaming Results ### Standardization - Community-Led Development - Additional Modalities - Standardization Process ## Protocol Details ### Message Format MCP uses JSON-RPC 2.0 for message exchange: 1. **Requests** ```json { "jsonrpc": "2.0", "id": "unique-id", "method": "method-name", "params": {} } ``` 2. **Responses** ```json { "jsonrpc": "2.0", "id": "unique-id", "result": {} } ``` 3. **Notifications** ```json { "jsonrpc": "2.0", "method": "notification-name", "params": {} } ``` ### Connection Lifecycle 1. **Initialization** - Client sends initialize request - Server responds with capabilities - Client sends initialized notification 2. **Operation** - Request/response exchanges - Notifications as needed - Tool execution - Resource access 3. **Termination** - Clean shutdown - Resource cleanup - Connection closure ## Implementation Examples ### Basic Server Example ```python from mcp.server import Server import mcp.types as types app = Server("example-server") @app.list_tools() async def list_tools() -> list[types.Tool]: return [ types.Tool( name="example-tool", description="An example tool", inputSchema={ "type": "object", "properties": { "param": {"type": "string"} } } ) ] @app.call_tool() async def call_tool(name: str, arguments: dict) -> str: if name == "example-tool": return f"Tool executed with: {arguments['param']}" raise ValueError(f"Unknown tool: {name}") ``` ### Basic Client Example ```typescript import { Client } from "@modelcontextprotocol/sdk/client"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio"; const client = new Client({ name: "example-client", version: "1.0.0" }); const transport = new StdioClientTransport({ command: "./server", args: [] }); await client.connect(transport); const tools = await client.listTools(); ``` ## Error Handling ### Standard Error Codes ```typescript enum ErrorCode { ParseError = -32700, InvalidRequest = -32600, MethodNotFound = -32601, InvalidParams = -32602, InternalError = -32603 } ``` ### Error Response Format ```json { "jsonrpc": "2.0", "id": "request-id", "error": { "code": -32700, "message": "Parse error", "data": {} } } ``` ### Error Handling Best Practices 1. **Client-Side** - Validate requests before sending - Handle timeouts appropriately - Implement retry logic - Log errors for debugging 2. **Server-Side** - Validate all inputs - Return appropriate error codes - Include helpful error messages - Maintain audit logs