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.
npm i mcp-summarization-functionsThis 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.
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
- 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
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)
-
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
The server supports multiple AI providers through 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 GoogleAPI_KEY: API key for the selected provider
MODEL_ID: Specific model to use (defaults to provider's standard model)PROVIDER_BASE_URL: Custom API endpoint for OpenAI-compatible providersMAX_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
# 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-expAdd 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"
}
}
}
}The server provides the following summarization tools:
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 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")
}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 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")
}Retrieve the full content for a given summary ID.
{
// Required
id: string // ID of the stored content
}MIT