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Context Optimizer MCP

@degenhero

リポジトリ

Context Optimizer MCP について

An MCP server that uses Redis and in-memory caching to optimize and extend context windows for large chat histories

#context-optimizer#mcp#chat-optimization

基本情報

カテゴリ

その他

ランタイム

node

トランスポート

stdio

公開者

degenhero

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "context-optimizer-mcp": {
      "command": "npx",
      "args": [
        "mcp",
        "install",
        "degenhero/context-optimizer-mcp"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Context Optimizer MCP?

Context Optimizer MCP is an MCP server that uses Redis and in-memory caching to optimize and extend context windows for large chat histories. It acts as a middleware between your application and LLM providers (currently supporting Anthropic's Claude models), automatically summarizing older messages to stay within token limits while preserving key information.

How to use Context Optimizer MCP?

Install using the MCP client (npx mcp install degenhero/context-optimizer-mcp), manually via npm install, or with Docker (docker-compose up -d). Configure environment variables in .env (Anthropic API key, Redis host/port, caching settings). Start the server, then send HTTP POST requests to http://localhost:3000/v1/messages using the standard Anthropic API format, optionally including conversation_id and context_optimization parameters.

Key features of Context Optimizer MCP

  • Dual-layer caching: in-memory LRU and persistent Redis
  • Automatic context summarization when nearing token limit
  • Rate limiting with burst protection
  • Drop-in replacement for Anthropic API
  • Built-in performance monitoring and metrics collection
  • Conversation continuity via unique conversation_id

Use cases of Context Optimizer MCP

  • Extending context for long-running chatbot sessions
  • Managing large conversation histories within token limits
  • Reducing API costs by caching summarized contexts
  • Maintaining context across multiple API calls for complex interactions

FAQ from Context Optimizer MCP

What are the prerequisites?

Node.js 18+, a Redis server (local or remote), and an Anthropic API key.

How do I configure it?

Copy .env.example to .env and set ANTHROPIC_API_KEY, Redis host/port/password, and caching parameters (e.g., IN_MEMORY_CACHE_MAX_SIZE). The server listens on port 3000 by default.

Does it support other LLM providers?

Currently, only Anthropic's Claude models are supported.

What transport does it use?

The server exposes an HTTP endpoint at /v1/messages that is compatible with the Anthropic API. It also provides /health and /metrics endpoints.

Where are conversation histories stored?

Summarized contexts are cached in both an in-memory LRU cache (for fast access) and Redis (for persistent, distributed storage across server instances).

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