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MCP LLM Integration Server

MCP LLM Integration Server

by raptor7197
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

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

Related MCP server: dottie-local

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

2 tools
echoA

Echo back the input text for testing

Inputs: text (required).

llm_predictC

Process text input through a local LLM

Inputs: prompt (required), max_tokens.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

    TDQS

    B3.2/5.0

    Scored across 2 tools

    Disambiguation5/5

    llm_predict and echo are completely distinct in purpose—one performs LLM inference while the other is a simple testing utility. There is no overlap or ambiguity between them.

    Naming Consistency3/5

    llm_predict uses a prefix-plus-verb compound while echo is a bare verb, showing mixed naming conventions. However, both names are short and readable, so the inconsistency is not chaotic.

    Tool Count3/5

    With only two tools, the server sits at the borderline of feeling thin. For a minimal LLM integration demo the pair is acceptable, but a broader integration server would likely need more endpoints.

    Completeness2/5

    The server provides only one functional inference operation plus an echo test tool. Obvious gaps exist for an 'LLM integration server,' such as model management, parameter options, or alternative interaction modes like embeddings or chat.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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