Summarization▌

--- ## Overview A powerful MCP server that provides intelligent summarization capabilities through a clean, extensible architecture. Built with modern TypeScript and designed for seamless integration with AI workflows. ## Installation ### Installing via Smithery To install Summarization Functions for Claude Desktop automatically via [Smithery](https://smithery.ai/server/mcp-summarization-functions): ```bash npx -y @smithery/cli install mcp-summarization-functions --client claude ``` ```bash npm i mcp-summarization-functions ``` ## AI Agent Integration This MCP server was primarily developed to enhance the performance and reliability of AI agents like Roo Cline and Cline. It addresses a critical challenge in AI agent operations: context window management. ### Context Window Optimization AI agents frequently encounter situations where their context window gets rapidly filled with large outputs from: - Command execution results - File content readings - Directory listings - API responses - Error messages and stack traces This server helps maintain efficient context usage by: 1. Providing concise, relevant summaries instead of full content 2. Storing full content for reference when needed 3. Offering focused analysis based on specific needs (security, API surface, etc.) 4. Supporting multiple output formats for optimal context utilization ### Benefits for AI Agents - **Reduced Failure Rates**: By preventing context window overflow - **Improved Response Quality**: Through focused, relevant summaries - **Enhanced Efficiency**: By maintaining important context while reducing noise - **Better Resource Management**: Through intelligent content caching and retrieval - **Flexible Integration**: Supporting multiple AI providers and configuration options ### Recommended AI Agent Prompt When integrating with AI agents, include the following in your agent's instructions: ``` # CONTEXT MANAGEMENT You have access to summarization functions through the MCP server. These functions are NOT optional - you MUST use them for ALL potentially large outputs to prevent context overflow: MANDATORY SUMMARIZATION: - You MUST ALWAYS use summarization functions for: - ANY first time file reading operations (unless you are CERTAIN its small and you are going to edit it) - ALL command execution outputs - EVERY directory analysis - ANY API responses or error logs - ANY output that could be large NEVER attempt to process raw output directly - ALWAYS use the appropriate summarization function: • For commands: summarize_command • For files: summarize_files • For directories: summarize_directory • For other text: summarize_text ALWAYS utilize available features: • Specify hints for focused analysis • Choose appropriate output formats • Use content IDs to access full details only when absolutely necessary There is NO NEED to process perfect or complete output. Summarized content is ALWAYS preferred over raw data. When in doubt, use summarization. ``` Summarization in action on the Ollama repository (Gemini 2.0 Flash summarization, Claude 3.5 agent)  ## Features - **Command Output Summarization** Execute commands and get concise summaries of their output - **File Content Analysis** Summarize single or multiple files while maintaining technical accuracy - **Directory Structure Understanding** Get clear overviews of complex directory structures - **Flexible Model Support** Use models from different providers - **AI Agent Context Optimization** Prevent context window overflow and improve AI agent performance through intelligent summarization ## Configuration The server supports multiple AI providers through environment variables: ### Required Environment Variables - `PROVIDER`: AI provider to use. Supported values: - `ANTHROPIC` - Claude models from Anthropic - `OPENAI` - GPT models from OpenAI - `OPENAI-COMPATIBLE` - OpenAI-compatible APIs (e.g. Azure) - `GOOGLE` - Gemini models from Google - `API_KEY`: API key for the selected provider ### Optional Environment Variables - `MODEL_ID`: Specific model to use (defaults to provider's standard model) - `PROVIDER_BASE_URL`: Custom API endpoint for OpenAI-compatible providers - `MAX_TOKENS`: Maximum tokens for model responses (default: 1024) - `SUMMARIZATION_CHAR_THRESHOLD`: Character count threshold for when to summarize (default: 512) - `SUMMARIZATION_CACHE_MAX_AGE`: Cache duration in milliseconds (default: 3600000 - 1 hour) - `MCP_WORKING_DIR` - fallback directory for trying to find files with relative paths from ### Example Configurations ```bash # Anthropic Configuration PROVIDER=ANTHROPIC API_KEY=your-anthropic-key MODEL_ID=claude-3-5-sonnet-20241022 # OpenAI Configuration PROVIDER=OPENAI API_KEY=your-openai-key MODEL_ID=gpt-4-turbo-preview # Azure OpenAI Configuration PROVIDER=OPENAI-COMPATIBLE API_KEY=your-azure-key PROVIDER_BASE_URL=https://your-resource.openai.azure.com MODEL_ID=your-deployment-name # Google Configuration PROVIDER=GOOGLE API_KEY=your-google-key MODEL_ID=gemini-2.0-flash-exp ``` ## Usage Add the server to your MCP configuration file: ```json { "mcpServers": { "MUST_USE_summarization": { "command": "node", "args": ["path/to/summarization-functions/build/index.js"], "env": { "PROVIDER": "ANTHROPIC", "API_KEY": "your-api-key", "MODEL_ID": "claude-3-5-sonnet-20241022", "MCP_WORKING_DIR": "default_working_directory" } } } } ``` ### Available Functions The server provides the following summarization tools: #### `summarize_command` Execute and summarize command output. ```typescript { // Required command: string, // Command to execute cwd: string, // Working directory for command execution // Optional hint?: string, // Focus area: "security_analysis" | "api_surface" | "error_handling" | "dependencies" | "type_definitions" output_format?: string // Format: "text" | "json" | "markdown" | "outline" (default: "text") } ``` #### `summarize_files` Summarize file contents. ```typescript { // Required paths: string[], // Array of file paths to summarize (relative to cwd) cwd: string, // Working directory for resolving file paths // Optional hint?: string, // Focus area: "security_analysis" | "api_surface" | "error_handling" | "dependencies" | "type_definitions" output_format?: string // Format: "text" | "json" | "markdown" | "outline" (default: "text") } ``` #### `summarize_directory` Get directory structure overview. ```typescript { // Required path: string, // Directory path to summarize (relative to cwd) cwd: string, // Working directory for resolving directory path // Optional recursive?: boolean, // Whether to include subdirectories. Safe for deep directories hint?: string, // Focus area: "security_analysis" | "api_surface" | "error_handling" | "dependencies" | "type_definitions" output_format?: string // Format: "text" | "json" | "markdown" | "outline" (default: "text") } ``` #### `summarize_text` Summarize arbitrary text content. ```typescript { // Required content: string, // Text content to summarize type: string, // Type of content (e.g., "log output", "API response") // Optional hint?: string, // Focus area: "security_analysis" | "api_surface" | "error_handling" | "dependencies" | "type_definitions" output_format?: string // Format: "text" | "json" | "markdown" | "outline" (default: "text") } ``` #### `get_full_content` Retrieve the full content for a given summary ID. ```typescript { // Required id: string // ID of the stored content } ``` ## Running evals The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found [here](https://www.mcpevals.io/docs). ```bash OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/server/mcp-server.ts ``` ## License MIT