mcp-server

# 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**: ```bash source .venv/bin/activate uv pip install mcp ``` 2. **Test the server**: ```bash 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`): ```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`): ```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: ```json { "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`: ```python 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: ```bash source .venv/bin/activate python main.py ``` Then send JSON-RPC requests via stdin: ```json {"jsonrpc": "2.0", "id": 1, "method": "initialize", "params": {"protocolVersion": "2024-11-05", "capabilities": {}, "clientInfo": {"name": "test-client", "version": "1.0.0"}}} ```