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    Mcp Summarization Functions

    Provides summarised output from various actions that could otherwise eat up tokens and cause crashes for AI agents

    36 stars
    TypeScript
    Updated Sep 30, 2025
    cline
    mcp
    mcp-server
    model-context-protocol

    Table of Contents

    • Intelligent text summarization for the Model Context Protocol
    • Overview
    • Installation
    • Installing via Smithery
    • AI Agent Integration
    • Context Window Optimization
    • Benefits for AI Agents
    • Recommended AI Agent Prompt
    • Features
    • Configuration
    • Required Environment Variables
    • Optional Environment Variables
    • Example Configurations
    • Usage
    • Available Functions
    • summarize_command
    • summarize_files
    • summarize_directory
    • summarize_text
    • get_full_content
    • Running evals
    • License

    Table of Contents

    • Intelligent text summarization for the Model Context Protocol
    • Overview
    • Installation
    • Installing via Smithery
    • AI Agent Integration
    • Context Window Optimization
    • Benefits for AI Agents
    • Recommended AI Agent Prompt
    • Features
    • Configuration
    • Required Environment Variables
    • Optional Environment Variables
    • Example Configurations
    • Usage
    • Available Functions
    • summarize_command
    • summarize_files
    • summarize_directory
    • summarize_text
    • get_full_content
    • Running evals
    • License

    Documentation

    ---

    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:

    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:

    code
    # 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)

    alt text

    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.

    bash
    OPENAI_API_KEY=your-key  npx mcp-eval src/evals/evals.ts src/server/mcp-server.ts

    License

    MIT

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