lean-docker-mcp

lean-docker-mcp

Dockerized Lean4 execution environment for AI agents.

Overview

This MCP server provides a safe, sandboxed Lean4 execution environment for LLM-powered agents. It allows agents to:

• Execute Lean4 code in isolated Docker containers
• Choose between transient or persistent execution environments
• Maintain state between execution steps

Installation

Requirements

• Docker must be installed and running on the host system
• Python 3.11 or later
• uv for package management (recommended)

Install from PyPI

# Using uv (recommended)
uv pip install lean-docker-mcp

# Using pip
pip install lean-docker-mcp

Install from Source

# Clone the repository
git clone https://github.com/artivus/lean-docker-mcp.git
cd lean-docker-mcp

# Install with uv
uv pip install -e .

# Or with pip
pip install -e .

Quick Start

Running the Server

The lean-docker-mcp server can be started directly using the module:

python -m lean_docker_mcp

This will start the MCP server and listen for JSONRPC requests on stdin/stdout.

Components

Docker Execution Environment

The server implements two types of execution environments:

1. Transient Environment

   • Each execution is isolated in a fresh container
   • State isn't maintained between calls
   • Safer for one-off code execution

2. Persistent Environment

   • Maintains state between executions
   • Variables and functions defined in one execution are available in subsequent executions
   • Suitable for interactive, stateful REPL-like sessions

Tools

The server provides the following tools:

• execute-lean: Run Lean4 code in a transient Docker container

  • Takes code (required) parameter
  • Returns execution results

• execute-lean-persistent: Run Lean4 code in a persistent Docker container

  • Takes code (required) and session_id (optional) parameters
  • Returns execution results
  • Maintains state between calls

• cleanup-session: Clean up a persistent session

  • Takes session_id (required) parameter
  • Stops and removes the associated Docker container

Configuration

The server can be configured via a YAML configuration file. By default, it looks for a file at ~/.lean-docker-mcp/config.yaml.

Configuration File Structure

Example configuration:

docker:
  image: lean-docker-mcp:latest
  working_dir: /home/leanuser/project
  memory_limit: 256m
  cpu_limit: 0.5
  timeout: 30
  network_disabled: true
  read_only: false

lean:
  allowed_imports:
    - Lean
    - Init
    - Std
    - Mathlib
  blocked_imports:
    - System.IO.Process
    - System.FilePath

Docker Configuration Options

Container Pooling

The Lean Docker MCP service includes a container pooling system to efficiently handle high-throughput environments. Pooling allows the service to:

1. Pre-create a pool of containers ready for immediate use
2. Reuse containers between executions (with full isolation between runs)
3. Limit the rate of container creation to avoid Docker rate limits
4. Scale gracefully for both single-agent and high-parallelism scenarios

How Container Pooling Works

• When the service starts, it initializes a pool of containers (configurable pool size)
• Each request gets a container from the pool instead of creating a new one
• After execution, containers are reset (processes killed, temp files removed) and returned to the pool
• Containers older than the max age setting are removed and replaced with fresh ones

Configuration Example

docker:
  # Standard Docker settings
  image: lean-docker-mcp:latest
  memory_limit: 256m
  cpu_limit: 0.5
  
  # Container pooling settings
  pool_enabled: true  # Enable container pooling
  pool_size: 32       # Keep up to 32 containers in the pool
  pool_max_age: 300   # Replace containers after 5 minutes (300 seconds)
  max_concurrent_creations: 5  # Limit parallel container creation 

When to Adjust Pool Settings

• High-traffic environments: Increase pool_size to handle more concurrent requests
• Memory-constrained hosts: Decrease pool_size or increase pool_max_age to reduce overhead
• Large clusters: Increase max_concurrent_creations if Docker can handle higher creation rates
• Single-agent use: Set pool_size to a small number (e.g., 5) for minimal resource usage
• No pooling: Set pool_enabled: false or pool_size: 0 to disable pooling entirely

Security Considerations

Container pooling maintains the same security guarantees as non-pooled execution:

• Each execution is completely isolated from previous ones
• All user state is wiped between executions
• The container is reset to a clean state after each use
• Security-related Docker settings (memory limits, CPU limits, network access) are preserved

Integration with Claude and Anthropic Products

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

"mcpServers": {
  "lean-docker-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/lean-docker-mcp",
      "run",
      "lean-docker-mcp"
    ]
  }
}

"mcpServers": {
  "lean-docker-mcp": {
    "command": "uvx",
    "args": [
      "lean-docker-mcp"
    ]
  }
}

"mcpServers": {
  "lean-docker-mcp": {
    "command": "uvx",
    "args": [
      "lean-docker-mcp"
    ],
    "env": {
      "LEAN_DOCKER_MCP_POOL_SIZE": "64",
      "LEAN_DOCKER_MCP_POOL_MAX_AGE": "600",
      "LEAN_DOCKER_MCP_MAX_CONCURRENT_CREATIONS": "10",
      "LEAN_DOCKER_MCP_POOL_ENABLED": "true",
      "LEAN_DOCKER_MCP_MEMORY_LIMIT": "512m",
      "LEAN_DOCKER_MCP_CPU_LIMIT": "0.8"
    }
  }
}

Environment Variable Configuration

You can configure the container pooling system and other settings using environment variables, which is especially useful in the MCP configuration files:

For high-scale RL training environments with many parallel agents, recommended settings:

"env": {
  "LEAN_DOCKER_MCP_POOL_SIZE": "64",
  "LEAN_DOCKER_MCP_MAX_CONCURRENT_CREATIONS": "10",
  "LEAN_DOCKER_MCP_POOL_MAX_AGE": "600"
}

For single-agent usage scenarios:

"env": {
  "LEAN_DOCKER_MCP_POOL_SIZE": "5",
  "LEAN_DOCKER_MCP_MAX_CONCURRENT_CREATIONS": "3"
}

Example MCP Usage

Transient Execution

# Define and use a simple function
result = await call_tool("execute-lean", {
  "code": "def hello (name : String) : String := s!\"Hello, {name}\"\n\ndef main : IO Unit := IO.println (hello \"Lean4!\")"
})

Persistent Session

# Create a persistent session and define a function
result = await call_tool("execute-lean-persistent", {
  "code": "def add (a b : Nat) : Nat := a + b\n\ndef main : IO Unit := IO.println \"Function defined\""
})

# Use the function in a subsequent call with the same session
result = await call_tool("execute-lean-persistent", {
  "session_id": "previous_session_id",
  "code": "def main : IO Unit := IO.println (toString (add 10 20))"
})

Development

Development Setup

1. Clone the repository:

git clone https://github.com/artivus/lean-docker-mcp.git
cd lean-docker-mcp

2. Set up development environment:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e ".[dev]"

3. Install pre-commit hooks:

pre-commit install

Running Tests

# Run all tests
pytest

# Run tests with coverage
pytest --cov=src/lean_docker_mcp

# Run specific test categories
pytest tests/unit/
pytest tests/integration/

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

3. Publish to PyPI:

uv publish

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /path/to/lean-docker-mcp run lean-docker-mcp

License

[License information]

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Performance Tuning

High-Scale RL Training

For environments running multiple parallel trajectories (like reinforcement learning trainers), use container pooling with these recommended settings:

{
  "mcpServers": {
    "lean-mcp": {
      "command": "uvx",
      "args": [
        "lean-docker-mcp"
      ],
      "env": {
        "LEAN_DOCKER_MCP_POOL_ENABLED": "true",
        "LEAN_DOCKER_MCP_POOL_SIZE": "64",
        "LEAN_DOCKER_MCP_POOL_MAX_AGE": "3600",
        "LEAN_DOCKER_MCP_MAX_CONCURRENT_CREATIONS": "10",
        "LEAN_DOCKER_MCP_MEMORY_LIMIT": "512m",
        "LEAN_DOCKER_MCP_CPU_LIMIT": "0.5"
      }
    }
  }
}

Key settings to adjust:

• LEAN_DOCKER_MCP_POOL_SIZE: Set this to your maximum expected concurrent trajectories
• LEAN_DOCKER_MCP_MAX_CONCURRENT_CREATIONS: Limit to avoid Docker rate limits
• LEAN_DOCKER_MCP_POOL_MAX_AGE: Increase for longer-lived containers (in seconds)
• LEAN_DOCKER_MCP_MEMORY_LIMIT: Adjust based on your cluster's resources

Local Development

For single-agent development on a local machine:

{
  "mcpServers": {
    "lean-mcp": {
      "command": "uv",
      "args": [
        "run",
        "-m",
        "lean_docker_mcp"
      ],
      "env": {
        "LEAN_DOCKER_MCP_POOL_ENABLED": "true",
        "LEAN_DOCKER_MCP_POOL_SIZE": "3",
        "LEAN_DOCKER_MCP_POOL_MAX_AGE": "1800",
        "LEAN_DOCKER_MCP_MAX_CONCURRENT_CREATIONS": "2"
      }
    }
  }
}

Setting Reasonable Values