MCP汇总函数

Summarization Functions

Intelligent text summarization for the Model Context Protocol

Features • AI Agent Integration • Installation • Usage

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:

npx -y @smithery/cli install mcp-summarization-functions --client claude

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:

Providing concise, relevant summaries instead of full content
Storing full content for reference when needed
Offering focused analysis based on specific needs (security, API surface, etc.)
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)

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

# 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:

{
		"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.

{
  // 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.

{
  // 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.

{
  // 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.

{
  // 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.

{
  // 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.

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

License

MIT

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

通过简洁、可扩展的架构提供智能摘要功能。主要用于解决大型存储库中 AI 代理的问题，因为大型文件可能会占用上下文窗口。

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