Modern Control Protocol (MCP) Server

Name: Modern Control Protocol (MCP) Server
Author: eagurin

@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

基本情報

カテゴリ

その他

トランスポート

stdio

公開者

eagurin

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"
      ]
    }
  }
}

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 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

FAQ from Modern Control Protocol (MCP) Server

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)."

What are the runtime dependencies?

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

How do I access the API documentation?

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

How is monitoring set up?

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

Is authentication/security mentioned?

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

「その他」の他のコンテンツ

Production-ready MCP integrations for AI applications

Klavis-AI

Klavis AI: MCP integration platforms that let AI agents use tools reliably at any scale

5.8K

Nginx UI

0xJacky

Yet another WebUI for Nginx

11.3K

Activepieces

activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

23.2K