mcp-server

raptor7197/mcp-server

3.2

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This document provides a comprehensive overview of the MCP LLM Integration Server, detailing its features, tools, resources, and usage across different platforms.

Tools

  1. llm_predict

    Process text input through a local LLM.

  2. echo

    Echo back the input text for testing.

MCP LLM Integration Server

This is a Model Context Protocol (MCP) server that allows you to integrate local LLM capabilities with MCP-compatible clients.

Features

  • llm_predict: Process text prompts through a local LLM
  • echo: Echo back text for testing purposes

Setup

  1. Install dependencies:

    source .venv/bin/activate
uv pip install mcp

  2. Test the server:

    python -c "
import asyncio
from main import server, list_tools, call_tool

async def test():
    tools = await list_tools()
    print(f'Available tools: {[t.name for t in tools]}')
    result = await call_tool('echo', {'text': 'Hello!'})
    print(f'Result: {result[0].text}')

asyncio.run(test())
"

Integration with LLM Clients

For Claude Desktop

Add this to your Claude Desktop configuration (~/.config/claude-desktop/claude_desktop_config.json):

{
  "mcpServers": {
    "llm-integration": {
      "command": "/home/tandoori/Desktop/dev/mcp-server/.venv/bin/python",
      "args": ["/home/tandoori/Desktop/dev/mcp-server/main.py"]
    }
  }
}

For Continue.dev

Add this to your Continue configuration (~/.continue/config.json):

{
  "mcpServers": [
    {
      "name": "llm-integration",
      "command": "/home/tandoori/Desktop/dev/mcp-server/.venv/bin/python",
      "args": ["/home/tandoori/Desktop/dev/mcp-server/main.py"]
    }
  ]
}

For Cline

Add this to your Cline MCP settings:

{
  "llm-integration": {
    "command": "/home/tandoori/Desktop/dev/mcp-server/.venv/bin/python",
    "args": ["/home/tandoori/Desktop/dev/mcp-server/main.py"]
  }
}

Customizing the LLM Integration

To integrate your own local LLM, modify the perform_llm_inference function in main.py:

async def perform_llm_inference(prompt: str, max_tokens: int = 100) -> str:
    Example: Using transformers
    from transformers import pipeline
    generator = pipeline('text-generation', model='your-model')
    result = generator(prompt, max_length=max_tokens)
    return result[0]['generated_text']
    
    Example: Using llama.cpp python bindings
    from llama_cpp import Llama
    llm = Llama(model_path="path/to/your/model.gguf")
    output = llm(prompt, max_tokens=max_tokens)
    return output['choices'][0]['text']
    
    Current placeholder implementation
    return f"Processed prompt: '{prompt}' (max_tokens: {max_tokens})"

Testing

Run the server directly to test JSON-RPC communication:

source .venv/bin/activate
python main.py

Then send JSON-RPC requests via stdin:

{"jsonrpc": "2.0", "id": 1, "method": "initialize", "params": {"protocolVersion": "2024-11-05", "capabilities": {}, "clientInfo": {"name": "test-client", "version": "1.0.0"}}}

Available Tools

llm_predict

  • Description: Process text input through a local LLM
  • Parameters:
    • prompt (required): The text prompt to send to the LLM
    • max_tokens (optional): Maximum number of tokens to generate (default: 100)

echo

  • Description: Echo back the input text for testing
  • Parameters:
    • text (required): Text to echo back

To Do

  1. Replace the placeholder LLM function with your actual model
  2. Configure your preferred LLM client to use this MCP server
  3. Test the integration with real prompts

Test

to test the present server create a virtual env and run the server/test.py file inside it