Slimcontext MCP Server

SlimContext MCP Server

A Model Context Protocol (MCP) server that wraps the SlimContext library, providing AI chat history compression tools for MCP-compatible clients.

Overview

SlimContext MCP Server exposes two powerful compression strategies as MCP tools:

1. `trim_messages` - Token-based compression that removes oldest messages when exceeding token thresholds
2. `summarize_messages` - AI-powered compression using OpenAI to create concise summaries

Installation

npm install -g slimcontext-mcp-server

or

pnpm add -g slimcontext-mcp-server

Development

# Clone and setup

git clone cd slimcontext-mcp-server pnpm install

Build

pnpm build

Run in development

pnpm dev

Type checking

pnpm typecheck

Configuration

MCP Client Setup

Add to your MCP client configuration:

{

"mcpServers": { "slimcontext": { "command": "npx", "args": ["-y", "slimcontext-mcp-server"] } } }

Environment Variables

• OPENAI_API_KEY: OpenAI API key for summarization (optional, can be passed as tool parameter)

Tools

trim_messages

Compresses chat history using token-based trimming strategy.

Parameters:

• messages (required): Array of chat messages
• maxModelTokens (optional): Maximum model token context window (default: 8192)
• thresholdPercent (optional): Percentage threshold to trigger compression 0-1 (default: 0.7)
• minRecentMessages (optional): Minimum recent messages to preserve (default: 2)

Example:

{

"messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Hello!" }, { "role": "assistant", "content": "Hi there! How can I help you today?" }, { "role": "user", "content": "Tell me about AI." } ], "maxModelTokens": 4000, "thresholdPercent": 0.8, "minRecentMessages": 2 }

Response:

{

"success": true, "original_message_count": 4, "compressed_message_count": 3, "messages_removed": 1, "compression_ratio": 0.75, "compressed_messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "assistant", "content": "Hi there! How can I help you today?" }, { "role": "user", "content": "Tell me about AI." } ] }

summarize_messages

Compresses chat history using AI-powered summarization strategy.

Parameters:

• messages (required): Array of chat messages
• maxModelTokens (optional): Maximum model token context window (default: 8192)
• thresholdPercent (optional): Percentage threshold to trigger compression 0-1 (default: 0.7)
• minRecentMessages (optional): Minimum recent messages to preserve (default: 4)
• openaiApiKey (optional): OpenAI API key (can also use OPENAI_API_KEY env var)
• openaiModel (optional): OpenAI model for summarization (default: 'gpt-4o-mini')
• customPrompt (optional): Custom summarization prompt

Example:

{

"messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "I want to build a web scraper." }, { "role": "assistant", "content": "I can help you build a web scraper! What programming language would you prefer?" }, { "role": "user", "content": "Python please." }, { "role": "assistant", "content": "Great choice! For Python web scraping, I recommend using requests and BeautifulSoup..." }, { "role": "user", "content": "Can you show me a simple example?" } ], "maxModelTokens": 4000, "thresholdPercent": 0.6, "minRecentMessages": 2, "openaiModel": "gpt-4o-mini" }

Response:

{

"success": true, "original_message_count": 6, "compressed_message_count": 4, "messages_removed": 2, "summary_generated": true, "compression_ratio": 0.67, "compressed_messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "system", "content": "The user expressed interest in building a web scraper and requested help with Python. The assistant recommended using requests and BeautifulSoup libraries for Python web scraping." }, { "role": "assistant", "content": "Great choice! For Python web scraping, I recommend using requests and BeautifulSoup..." }, { "role": "user", "content": "Can you show me a simple example?" } ] }

Message Format

Both tools expect messages in SlimContext format:

interface SlimContextMessage {

role: 'system' | 'user' | 'assistant' | 'tool' | 'human'; content: string; }

Error Handling

All tools return structured error responses:

{

"success": false, "error": "Error message description", "error_type": "SlimContextError" | "OpenAIError" | "UnknownError" }

Common error scenarios:

• Missing OpenAI API key for summarization
• Invalid message format
• OpenAI API rate limits or errors
• Invalid parameter values

Token Estimation

SlimContext uses a simple heuristic for token estimation: Math.ceil(content.length / 4) + 2. This provides a reasonable approximation for most use cases. For more accurate token counting, you would need to implement a custom token estimator in your client application.

Compression Strategies

Trimming Strategy

• Preserves all system messages
• Preserves the most recent N messages
• Removes oldest non-system messages until under token threshold
• Fast and deterministic
• No external API dependencies

Summarization Strategy

• Preserves all system messages
• Preserves the most recent N messages
• Summarizes middle portion of conversation using AI
• Creates contextually rich summaries
• Requires OpenAI API access

License

MIT

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

Related

• SlimContext - The underlying compression library
• Model Context Protocol - The protocol specification
• MCP SDK - TypeScript SDK for MCP