Manages environment variables for configuration, including Selector AI API keys and URLs.
Runs the MCP server in a lightweight container with health checks, and the client can spawn the Docker container for interactive sessions.
Used for cloning the repository during installation process.
Selector AI FastMCP
This repository provides a full implementation of the Model Context Protocol (MCP) for Selector AI. It includes a streaming-capable server and a Docker-based interactive client that communicates via stdin/stdout.
✨ Features
✅ Server
FastMCP-compatible and built on Python
Real-time SSE streaming support
Interactive AI chat with Selector AI
Minimal boilerplate
Built-in health check for container orchestration
Request/response logging and retries
✅ Client
Python client spawns server via Docker
Supports both CLI and programmatic access
Reads/writes via stdin and stdout
Environment variable configuration using .env
🚀 Quick Start
Prerequisites
Python 3.8+
Docker
A Selector AI API Key
Selector API URL
⚙️ Installation
Clone the Repository
git clone https://github.com/automateyournetwork/selector-mcp-server
cd selector-ai-mcp
Install Python Dependencies
pip install -r requirements.txt
Set Environment Variables Create a .env file:
SELECTOR_URL=https://your-selector-api-url
SELECTOR_AI_API_KEY=your-api-key
🐳 Dockerfile
The server runs in a lightweight container using the following Dockerfile:
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "-u", "mcp_server.py"]
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s
CMD python -c "import socket; s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM); s.connect('/tmp/mcp.sock'); s.send(b'{"tool_name": "ready"}\n'); data = s.recv(1024); s.close(); import json; result = json.loads(data); exit(0 if result.get('status') == 'ready' else 1)" || exit 1
Build the Docker Image
docker build -t selector-mcp .
🧠 Using the Client
Start the Client
This will spawn the Docker container and open an interactive shell.
python mcp_client.py
Example CLI Session
You> What is AIOps?
Selector> AIOps refers to the application of AI to IT operations...
Programmatic Access
from selector_client import call_tool, spawn_server
proc = spawn_server()
call_tool(proc, "ready")
response = call_tool(proc, "ask_selector", {"content": "What is AIOps?"})
print(response)
🖥️ Using with Claude Desktop
If you're integrating with Claude Desktop, you can run this server and expose a socket or HTTP endpoint locally:
Run the server using Docker or natively:
python mcp_server.py
Connect to the socket or HTTP endpoint from Claude Desktop's external tool configuration.
Ensure your messages match the format:
Claude Desktop will receive the AI's structured response via stdout.
🛠️ Build Your Own Container
To customize this setup:
Fork or clone this repo
Modify the selector_fastmcp_server.py to integrate your preferred model or routing logic
Rebuild the Docker image:
docker build -t my-custom-mcp .
Update the client to spawn my-custom-mcp instead:
"docker", "run", "-i", "--rm", "my-custom-mcp"
📁 Project Structure
selector-ai-mcp/
✅ Requirements
Dependencies in requirements.txt:
requests
python-dotenv
📜 License
Apache License 2.0
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A Model Context Protocol (MCP) server that enables real-time, interactive AI chat with Selector AI through a streaming-capable server and Docker-based client communicating via stdin/stdout.