MCP TriliumNext

# MCP (Model Context Protocol) Fundamentals ## What is MCP? Model Context Protocol (MCP) is a framework for standardizing interactions between clients and language models through a structured, flexible protocol. It enables reusable, dynamic prompt templates and workflows while giving users explicit control over model interactions. ## Core Architecture Components ### 1. **Tools** - Extensible capabilities that enhance model interactions - Functions that the LLM can call to perform actions - Examples: file operations, API calls, database queries - Must be explicitly defined with input schemas ### 2. **Resources** - Contextual data that can be dynamically integrated into prompts - Support various data types and sources - Can be referenced in prompts and tools - Examples: file contents, database records, API responses ### 3. **Prompts** - Predefined templates that can accept dynamic arguments - Include context from resources - Chain multiple interactions - Guide specific workflows - Enable standardized AI interactions ### 4. **Sampling** - Allows servers to request LLM completions dynamically - Enables "intermediate reasoning steps" and multi-turn workflows - Provides human-in-the-loop approval and visibility ## Key Design Principles ### User Control - User-controlled interactions with explicit approval steps - Host can review/modify prompts before execution - Input validation and security controls ### Composability - Flexible and composable architecture - Support for complex, multi-step workflows - Model-agnostic approach ### Security - Secure and controlled model interactions - User visibility into all operations - Input validation for prompts and tools ## Implementation Patterns ### Server Structure ```javascript class MCPServer { constructor() { this.server = new Server({name, version}, {capabilities}); this.setupToolHandlers(); this.setupResourceHandlers(); } setupToolHandlers() { // Define available tools with schemas } setupResourceHandlers() { // Define available resources } } ``` ### Workflow Patterns - **Parallel**: Distributing tasks across multiple sub-agents - **AugmentedLLM**: Base workflow for tool-enhanced language models - **Multi-agent**: Collaborative workflows between agents ## Best Practices ### Tool Design - Use clear, descriptive tool names and descriptions - Define comprehensive input schemas with validation - Handle errors gracefully with meaningful messages - Limit scope to specific, well-defined functions ### Prompt Engineering - Use clear system prompts - Structure responses for easy parsing - Include only relevant context - Respect token usage limits ### Security Considerations - Implement input validation for all parameters - Provide user-visible approval steps for sensitive operations - Use authentication tokens securely - Avoid exposing sensitive data in logs ### Performance - Limit token usage in prompts and responses - Cache frequently accessed resources - Use async operations where possible - Handle timeouts and retries appropriately ## Common Use Cases 1. **Note-taking Integration** (like TriliumNext) - Create, read, update, delete notes - Search and query functionality - Content organization and tagging 2. **Development Tools** - Code analysis and generation - Git operations - Build and deployment automation 3. **Data Processing** - Database queries and updates - File system operations - API integrations ## Integration Approaches ### Standalone Server - Command-line tools that accept stdin/stdout - Can be integrated with various MCP clients - Portable across different environments ### Client Integration - Claude Desktop MCP configuration - Custom client applications - Browser-based tools with MCP Inspector ## Development Workflow 1. **Design Phase** - Define tools and resources needed - Plan authentication and security model - Design input/output schemas 2. **Implementation** - Set up basic server structure - Implement individual tools with proper error handling - Add resource handlers for data access 3. **Testing** - Use MCP Inspector for interactive testing - Test all tools with various inputs - Verify error handling and edge cases 4. **Deployment** - Configure environment variables - Set up authentication - Integrate with target MCP clients ## Goals and Philosophy MCP aims to create "AI agents that are portable, decoupled, secure, observable, and controlled." The focus is on: - **Portability**: Servers work across different clients and environments - **Decoupling**: Clear separation between tools, resources, and prompts - **Security**: User control and visibility into all operations - **Observability**: Clear logging and debugging capabilities - **Control**: User approval and oversight of agent actions