Modern Control Protocol (MCP) Server

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.