Modern Control Protocol (MCP) Server
About
A modern, scalable MCP server implementation with support for multiple AI providers, advanced monitoring, and robust conversation management.
Details
- Author
- eagurin
- Downloads
- 316
- Categories
- Other
Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Modern Control Protocol (MCP) ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"modern control protocol (mcp) server": {
"mymcpserv": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"mymcpserv": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}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
.env4. Start services with Docker Compose:
docker-compose up -d
API Documentation
Once running, visit:
- API Documentation: http://localhost:8000/docs
- ReDoc Documentation: http://localhost:8000/redoc
Monitoring
- Prometheus metrics: http://localhost:9090
- Grafana dashboards: http://localhost:3000
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
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