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Modern Control Protocol (MCP) Server

@eagurin

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基本情報

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その他

トランスポート

stdio

作者

eagurin

Modern Control Protocol (MCP) Server について

A modern, scalable MCP server implementation with support for multiple AI providers, advanced monitoring, and robust conversation management.

#mcp#ai-server#conversation-management

Modern Control Protocol (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": {
    "mymcpserv": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Modern Control Protocol (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。

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 Modern Control Protocol (MCP) Server?

A modern, scalable MCP server implementation with support for multiple AI providers (OpenAI, Anthropic, Google AI, Azure), advanced monitoring, and robust conversation management. It targets developers building AI-powered applications that need multi-provider integration, streaming, and conversation history.

How to use Modern Control Protocol (MCP) Server?

Clone the repository, copy .env.example to .env, update environment variables, then start services with docker-compose up -d. For local development, create a Python virtual environment, install dependencies from requirements.txt, and run uvicorn app.main:app --reload.

Key features of Modern Control Protocol (MCP) Server

  • Multi-provider AI support (OpenAI, Anthropic, Google AI, Azure)
  • Real-time streaming responses
  • Conversation management and history
  • Function calling and tool usage
  • Vector database integration (Qdrant)
  • Semantic caching with Redis
  • Prometheus metrics and Grafana dashboards
  • Rate limiting and error handling
  • PostgreSQL for data persistence
  • Elasticsearch for search
  • Docker containerization

Use cases of Modern Control Protocol (MCP) Server

  • Building chatbots that switch between AI providers based on cost or capability
  • Deploying a production-grade conversation API with caching and rate limiting
  • Implementing semantic search over conversation history using Elasticsearch and vectors
  • Monitoring AI service performance and usage via Prometheus/Grafana

よくある質問

1

What AI providers are supported?

OpenAI, Anthropic, Google AI, and Azure (Azure OpenAI likely included, though not explicitly stated in the README). The README lists "Multi-provider AI support (OpenAI, Anthropic, Google AI, Azure)."

2

What are the runtime dependencies?

Python 3.9+, PostgreSQL, Redis, Elasticsearch, and Docker & Docker Compose for containerized deployment.

3

How do I access the API documentation?

Once running, API docs are at `http://localhost:8000/docs` and ReDoc at `http://localhost:8000/redoc`.

4

How is monitoring set up?

Prometheus metrics are available at `http://localhost:9090` and Grafana dashboards at `http://localhost:3000`.

5

Is authentication/security mentioned?

The README does not mention authentication, authorization, or transport security. It only notes rate limiting and error handling as features.

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