Gym MCP Server

Expose any Gymnasium environment as an MCP (Model Context Protocol) server, automatically converting the Gym API (reset, step, render) into MCP tools that any agent can call via standard JSON interfaces.

Features

• 🎮 Works with any Gymnasium environment
• 🔧 Exposes gym operations as MCP tools (reset, step, render, etc.)
• 🚀 Simple API with automatic serialization and error handling
• 🤖 Designed for AI agent integration (OpenAI Agents SDK, LangChain, etc.)
• 🔍 Type safe with full type hints

Installation

pip install gym-mcp-server

Requirements: Python 3.10+

Quick Start

MCP Server

Run the server with the standard MCP protocol:

# Using stdio transport (default)
python -m gym_mcp_server --env CartPole-v1 --transport stdio

# Using HTTP transport
python -m gym_mcp_server --env CartPole-v1 --transport streamable-http --host localhost --port 8000

# Using SSE transport
python -m gym_mcp_server --env CartPole-v1 --transport sse --host localhost --port 8000

Programmatic Usage

from gym_mcp_server import GymMCPServer

# Create an MCP server with stdio transport
server = GymMCPServer(
    env_id="CartPole-v1",
    render_mode="rgb_array"
)

# Run the server (blocking call)
# server.run(transport="stdio")

# Or with HTTP transport
server_http = GymMCPServer(
    env_id="CartPole-v1",
    host="localhost",
    port=8000
)
# server_http.run(transport="streamable-http")

Available Tools

The server exposes these MCP tools:

• reset_env - Reset to initial state (optional seed)
• step_env - Take an action (required action)
• render_env - Render current state (optional mode)
• close_env - Close environment and free resources
• get_env_info - Get environment metadata
• get_available_tools - List all available tools

All tools return a standardized format:

{
    "success": bool,  # Whether the operation succeeded
    "error": str,     # Error message (if success=False)
    # ... tool-specific data
}

Examples

The examples/ directory contains complete working examples:

• MCP Server - Creating and running MCP servers
• MCP Client - Low-level MCP protocol usage
• OpenAI Agents SDK (stdio) - AI agent with stdio transport
• OpenAI Agents SDK (HTTP) - AI agent with HTTP transport

See examples/README.md for details and instructions.

Integration

OpenAI Agents SDK

Use the MCPServerStdio or MCPServerStreamableHttp classes to connect agents to gym environments:

from agents import Agent, Runner
from agents.mcp import MCPServerStdio

async with MCPServerStdio(
    name="Gym Environment",
    params={"command": "python", "args": ["-m", "gym_mcp_server", "--env", "CartPole-v1"]},
) as server:
    agent = Agent(name="GymAgent", instructions="...", mcp_servers=[server])
    result = await Runner.run(agent, "Play CartPole")

See examples/openai_agents_stdio_example.py and examples/openai_agents_http_example.py.

Documentation: OpenAI Agents SDK MCP Integration

Other Frameworks

Compatible with any MCP-compatible framework (LangChain, AutoGPT, custom MCP clients, etc.)

Configuration

Command Line Options

python -m gym_mcp_server --help

• --env: Gymnasium environment ID (required)
• --render-mode: Default render mode (e.g., rgb_array, human)
• --transport: Transport type - stdio, streamable-http, or sse (default: stdio)
• --host: Host for HTTP-based transports (default: localhost)
• --port: Port for HTTP-based transports (default: 8000)

Transport Options

The server supports multiple transport mechanisms:

stdio (Default): Standard input/output, suitable for local MCP clients

python -m gym_mcp_server --env CartPole-v1 --transport stdio

streamable-http: HTTP-based transport with streaming support

python -m gym_mcp_server --env CartPole-v1 --transport streamable-http --host 0.0.0.0 --port 8000

sse: Server-Sent Events transport for real-time updates

python -m gym_mcp_server --env CartPole-v1 --transport sse --host 0.0.0.0 --port 8000

Troubleshooting

Environment-Specific Dependencies

Some environments require additional packages:

pip install gymnasium[atari]   # For Atari environments
pip install gymnasium[box2d]   # For Box2D environments
pip install gymnasium[mujoco]  # For MuJoCo environments

Python Version

Ensure you're using Python 3.10+:

python --version  # Should show 3.10 or higher

Development

For development and testing:

git clone https://github.com/haggaishachar/gym-mcp-server.git
cd gym-mcp-server
make install     # Install with dependencies
make check       # Run all checks (format, lint, typecheck, test)

See the Makefile for all available commands.

License

MIT License - see the LICENSE file for details.

Links

• GitHub Repository
• Gymnasium Documentation
• Model Context Protocol