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llm-mcp

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A Ruby gem for integrating Large Language Models (LLMs) via the Model Context Protocol (MCP) into development workflows.

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llm-mcp

A Ruby gem that exposes Large Language Models (LLMs) via the Model Context Protocol (MCP), enabling seamless integration of AI capabilities into your development workflow.

Overview

llm-mcp creates an MCP server that provides standardized access to various LLM providers (OpenAI, Google Gemini, and OpenAI-compatible APIs) while supporting advanced features like session management, conversation persistence, and integration with external MCP tools.

Key Features

  • 🤖 Multi-Provider Support: Works with OpenAI, Google Gemini, and any OpenAI-compatible API
  • 💬 Session Management: Persist conversations across server restarts
  • 🔧 MCP Tool Integration: Connect to external MCP servers and use their tools within LLM conversations
  • 📝 Comprehensive Logging: JSON-formatted logs for debugging and analysis
  • 🔌 Extensible Architecture: Easy to add new providers and customize behavior
  • 🚀 Built on FastMCP: Leverages the fast and efficient MCP server framework

MCP Tools Exposed

task

Send a request to the LLM and get a response.

Parameters:

  • prompt (required): The message or question for the LLM
  • temperature (optional): Control randomness (0.0-2.0, default: 0.7)
  • max_tokens (optional): Maximum response length

Example Request:

{
  "method": "tools/call",
  "params": {
    "name": "task",
    "arguments": {
      "prompt": "Explain the concept of dependency injection",
      "temperature": 0.7,
      "max_tokens": 500
    }
  }
}

reset_session

Clear the conversation history and start fresh.

Example Request:

{
  "method": "tools/call",
  "params": {
    "name": "reset_session",
    "arguments": {}
  }
}

Session Management

Sessions automatically persist conversations to disk, allowing you to:

  • Resume previous conversations
  • Maintain context across server restarts
  • Track token usage over time

Sessions are stored in ~/.llm-mcp/sessions/ by default, with each session saved as a JSON file.

Session Files

Session files contain:

  • Message history (user, assistant, and system messages)
  • Timestamps for each interaction
  • Token usage statistics
  • Session metadata

Logging

Enable JSON logging for comprehensive debugging:

llm-mcp mcp-serve \
  --provider openai \
  --model gpt-4 \
  --json-log-path logs/llm.json \
  --verbose

Logs include:

  • All requests and responses
  • Tool calls and their results
  • Session operations
  • Error messages and stack traces

Integration Examples

Using with Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "llm-mcp": {
      "command": "llm-mcp",
      "args": ["mcp-serve", "--provider", "openai", "--model", "gpt-4"],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

Using with mcp-client

require 'mcp-client'

client = MCP::Client.new
client.connect_stdio('llm-mcp', 'mcp-serve', '--provider', 'openai', '--model', 'gpt-4')

# Use the task tool
response = client.call_tool('task', {
  prompt: "Write a haiku about Ruby programming",
  temperature: 0.9
})

puts response.content

Combining Multiple MCP Servers

Create a powerful AI assistant by combining llm-mcp with other MCP servers:

{
  "mcpServers": {
    "llm": {
      "command": "llm-mcp",
      "args": ["mcp-serve", "--provider", "openai", "--model", "gpt-4", "--mcp-config", "mcp-tools.json"]
    },
    "filesystem": {
      "command": "mcp-filesystem",
      "args": ["/project"]
    },
    "git": {
      "command": "mcp-git"
    }
  }
}

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests.

# Install dependencies
bundle install

# Run tests
bundle exec rake test

# Run linter
bundle exec rubocop -A

# Install gem locally
bundle exec rake install