inooLabs/intric-mcp-template
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The Intric MCP Template Server is designed to facilitate the creation of Model Context Protocol servers that integrate with Intric's MCP client, enhancing AI assistant capabilities.
Intric MCP Template Server
A template for building Model Context Protocol (MCP) servers that connect seamlessly with Intric's built-in MCP client. This template demonstrates how to create custom tools and resources that extend your AI assistant's capabilities.
Features
- Tools: Functions the AI can call to perform actions
- Resources: Static data the AI can access
- Resource Templates: Dynamic resources based on parameters
Running the Server
Start the MCP server with HTTP transport:
python server.py
The server will start on http://localhost:8000 by default.
Connecting to Intric
Add your exposed server URL in Intric's MCP connections settings. Intric will automatically discover all available tools and resources.
Tip: You can use a service like ngrok to expose a https url binded to a local port and put that url (ending with /mcp) into Intric for testing.
Building Your Own MCP Server
Adding Tools
@mcp.tool
def your_function_name(param1: str, param2: int) -> str:
"""Description of what this tool does."""
return f"Result: {param1} - {param2}"
Adding Resources
@mcp.resource("resource://your_resource_name")
def get_your_data() -> str:
"""Description of what data this resource provides"""
return "Your data here"
Adding Resource Templates
@mcp.resource("data://{category}/{id}")
def get_dynamic_data(category: str, id: str) -> dict:
"""Provide data based on category and id."""
return {"category": category, "id": id, "data": "..."}
Project Structure
intric-mcp-template/
├── server.py # Main server file with examples
├── tools.py # Example tool implementations
├── resources.py # Example resource implementations
├── requirements.txt # Python dependencies