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Modern Control Protocol (MCP) Server

Modern Control Protocol (MCP) Server

Local
github.com/eagurinAI & Agents0Updated 90w ago
Repository

About Modern Control Protocol (MCP) Server

An MCP server for AI applications that manages conversations, invokes AI providers, and provides monitoring. It combines streaming responses with semantic caching and persistence and search components.

Key features

  1. 01Multiple AI provider support
  2. 02Real-time streaming responses
  3. 03Conversation and history management
  4. 04Function calling and tool use
  5. 05Semantic caching and vector storage
  6. 06Prometheus metrics and Grafana dashboards

Use cases

  1. 01Building AI conversation services with multiple providers
  2. 02Tracking service metrics and viewing dashboards
  3. 03Persisting and searching conversation history

MCP support

The server provides MCP prompt management, conversation handling, and AI completion endpoints for MCP clients.

#mcp#ai-server#conversation-management

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Modern Control Protocol (MCP) Server

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

Features

  • 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
  • Semantic caching
  • Prometheus metrics and Grafana dashboards
  • Rate limiting and error handling
  • PostgreSQL for data persistence
  • Redis for caching
  • Elasticsearch for search
  • Docker containerization

Quick Start

  1. Clone the repository
  2. Copy environment template: 
    cp .env.example .env
  3. Update environment variables in .env
  4. Start services with Docker Compose: 
    docker-compose up -d

API Documentation

Once running, visit:

Monitoring

Development

Prerequisites
  • Python 3.9+
  • PostgreSQL
  • Redis
  • Elasticsearch
  • Docker & Docker Compose
Local Setup

  1. Create virtual environment:

    python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run development server:

    uvicorn app.main:app --reload
Testing

Run tests with:

pytest

Architecture

The MCP server is built with a microservices architecture:

  • FastAPI for the REST API
  • PostgreSQL for data persistence
  • Redis for caching and rate limiting
  • Elasticsearch for search functionality
  • Qdrant for vector storage
  • Prometheus and Grafana for monitoring

API Endpoints

  • /api/v1/mcp/prompts: Prompt management
  • /api/v1/mcp/conversations: Conversation handling
  • /api/v1/mcp/conversations/{id}/complete: AI completions
  • /metrics: Prometheus metrics
  • /health: Health check

License

MIT License

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.