Modern Control Protocol (MCP) Server

@eagurin

Basic information

About 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

Modern Control Protocol (MCP) Server config

Add this server to your MCP-compatible client using the configuration below.

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

Modern Control Protocol (MCP) Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

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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