Gym Server
FreeNot checkedExpose any Gymnasium environment as an MCP server, automatically converting the Gym API into MCP tools that any agent can call via standard JSON interfaces.
About
Expose any Gymnasium environment as an MCP server, automatically converting the Gym API into MCP tools that any agent can call via standard JSON interfaces.
README
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.
Python 3.10+ License: MIT Test Coverage
Features
- 🎮 Works with any Gymnasium environment
- 🔧 Exposes gym operations via multiple protocols:
- MCP (Model Context Protocol) over HTTP (
/mcp, streamable-http) - HTTP/REST - FastAPI with Swagger UI (same server)
- MCP (Model Context Protocol) over HTTP (
- 🚀 Simple API with automatic serialization and error handling
- 🤖 Designed for AI agent integration (OpenAI Agents SDK, LangChain, etc.)
- 🔍 Type safe with full type hints
- ♻️ Shared service layer for code reuse across protocols
Installation
pip install gym-mcp-server
Requirements: Python 3.10+
Quick Start
Combined HTTP server (REST + MCP)
Run a single server that exposes both REST endpoints and the MCP endpoint:
python -m gym_mcp_server --env CartPole-v1 --host localhost --port 8000
# REST docs: http://localhost:8000/docs
# MCP endpoint: http://localhost:8000/mcp
Programmatic Usage
from gym_mcp_server import GymHTTPServer
# One HTTP server exposing both REST + MCP (/mcp) for the same env instance
server = GymHTTPServer(env_id="CartPole-v1", render_mode="rgb_array")
# server.run(host="localhost", port=8000)
Available Tools
The server exposes these MCP tools:
reset_env- Reset to initial state (optionalseed)step_env- Take an action (requiredaction)render_env- Render current state (optionalmode)close_env- Close environment and free resourcesget_env_info- Get environment metadatasample_action- Sample a random action from the action space
All tools return a standardized format:
{
"success": bool, # Whether the operation succeeded
"error": str, # Error message (if success=False)
# ... tool-specific data
}
Examples
You can use the server with any MCP-compatible client. Here's a simple example using the MCP Python client:
from mcp import ClientSession
from mcp.client.streamable_http import streamable_http_client
async with streamable_http_client("http://localhost:8000/mcp") as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
# List available tools
tools = await session.list_tools()
print(f"Available tools: {[tool.name for tool in tools.tools]}")
# Reset the environment
result = await session.call_tool("reset_env", arguments={})
print(f"Reset result: {result.content[0].text}")
Integration
OpenAI Agents SDK
Use the MCPServerStreamableHttp class to connect agents to gym environments:
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async with MCPServerStreamableHttp(
name="Gym Environment",
params={"url": "http://localhost:8000/mcp", "timeout": 10},
) as server:
agent = Agent(name="GymAgent", instructions="...", mcp_servers=[server])
result = await Runner.run(agent, "Play CartPole")
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)--host: Host to bind (default: localhost)--port: Port to bind (default: 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 (lint, typecheck, test)
See the Makefile for all available commands.
License
MIT License - see the LICENSE file for details.
Links
Install Gym Server in Claude Desktop, Claude Code & Cursor
unyly install gym-mcp-serverInstalls into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.
First time? Get the CLI: curl -fsSL https://unyly.org/install | sh
Or configure manually
Run in your terminal:
claude mcp add gym-mcp-server -- uvx gym-mcp-serverFAQ
Is Gym Server MCP free?
Yes, Gym Server MCP is free — one-click install via Unyly at no cost.
Does Gym Server need an API key?
No, Gym Server runs without API keys or environment variables.
Is Gym Server hosted or self-hosted?
A hosted option is available: Unyly runs the server in the cloud, no local setup required.
How do I install Gym Server in Claude Desktop, Claude Code or Cursor?
Open Gym Server on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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