広告掲載
MCP.so
ログイン

Context Optimizer MCP Server

@degenhero

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

degenhero

Context Optimizer MCP Server 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

Context Optimizer MCP Server の設定

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"
      ]
    }
  }
}

Context Optimizer MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

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

よくある質問

1

What are the prerequisites?

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

2

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.

3

Does it support other LLM providers?

Currently, only Anthropic's Claude models are supported.

4

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.

5

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).

コメント

「その他」の他のコンテンツ

Lemon.io

lemon-io

Request dedicated senior developers to work on your project, write job descriptions, and prep technical interviews — all without leaving your Claude chat. Just describe what you need, and Lemon.io MCP does the rest. 1–3

MCP Toolbox for Databases

googleapis

MCP Toolbox for Databases is an open source MCP server for databases.

15.9K

🚀 Model Context Protocol (MCP) Curriculum for Beginners

microsoft

This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,

16.7K

DeepMark

DeepMark helps teachers deliver rapid, consistent marking with meaningful feedback for every student — in a fraction of the time. What once took a week, now takes one free period.

Awesome Mlops

visenger

A curated list of references for MLOps

14.0K
広告掲載