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Context Optimizer MCP Server

@degenhero

代码仓库

基本信息

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其他

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stdio

作者

degenhero

关于 Context Optimizer MCP Server

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Context Optimizer MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

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

常见问题

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).

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