Modern Control Protocol (MCP) Server

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

@eagurin

关于 Modern Control Protocol (MCP) Server

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

#mcp #ai-server #conversation-management

基本信息

分类

其他

传输方式

stdio

发布者

eagurin

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mymcpserv": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

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