Mujoco

Python MuJoCo Tests License: MIT

65 MCP tools that expose MuJoCo physics simulation to Claude Code and any Model Context Protocol client.

Load robots, step physics, analyze contacts, optimize trajectories, export videos — all through natural language in your AI assistant.

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Requirements

• Python ≥ 3.10, MuJoCo ≥ 2.3
• Linux with EGL (GPU) or OSMesa (CPU headless) for rendering
• uv recommended

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Quick Start

git clone https://github.com/Rongxuan-Zhou/mujoco-mcp-server.git
cd mujoco-mcp-server
pip install -e .

Add to Claude Code

claude mcp add mujoco-sim -- uv run --directory /path/to/mujoco-mcp-server mujoco-mcp

Or manually in ~/.claude.json:

{
  "mcpServers": {
    "mujoco-sim": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mujoco-mcp-server", "mujoco-mcp"],
      "env": { "MUJOCO_GL": "egl" }
    }
  }
}

HTTP Transport (remote)

python -m mujoco_mcp --transport streamable-http --host 0.0.0.0 --port 8080

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What you can do

Once connected, just ask Claude:

Trajectory optimization & control

"Run iLQR on the Franka arm to reach [0.4, 0, 0.5] in 50 steps — show the control sequence and waypoints"
"Compare MPPI (200 samples) vs iLQR on the hopper reaching task; which converges to lower cost?"
"Plan a min-jerk Cartesian trajectory for the end-effector, step the PD controller, and plot joint torques over time"

Robustness & sim-to-real

"Sweep body mass ±40% and floor friction [0.1, 2.0] over 50 random samples — report mean/std of max CoM speed"
"Apply 30 N lateral impulses across 16 Fibonacci sphere directions; what is the stability margin of the current controller?"
"At what force magnitude does the bipedal walker fail to recover in more than 25% of directions?"

Contact mechanics & model validation

"Validate this MJCF for dangling actuators and solref instabilities before I load it"
"Are the contact parameters between the gripper fingers and the object causing numerical stiffness? Suggest alternatives"
"Show the active contact forces and check whether the friction cone constraints are satisfied"

Kinematics & workspace analysis

"Compute the manipulability index at this Franka configuration — is it near a singularity?"
"Solve IK for site 'ee' at [0.5, 0.2, 0.3] and verify the joint limits are respected"
"Map reachable workspace by sweeping joint angles and recording Cartesian ee positions"

RL & data pipelines

"Set up a Gymnasium env for the hopper, run 500 steps with random actions, and export a phase portrait of hip angle vs angular velocity"
"Record a 10-second rollout, export full state log with body positions and sensor data, then plot the 3D CoM trajectory"
"Run domain randomization on link masses and timestep — export CSV and show which parameter range produces the worst instability"

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Tools

Group	Tools	Description
Simulation	sim_load sim_step sim_forward sim_reset sim_get_state sim_set_state sim_record sim_list	Load MJCF/XML models and step physics
Rendering	render_snapshot render_depth	PNG snapshots and depth maps
Analysis	analyze_contacts analyze_energy analyze_forces compute_jacobian compute_derivatives read_sensors	Contacts, energy, forces, Jacobians, linearized dynamics
Model	modify_model reload_from_xml	In-place parameter edits (no recompile) or full XML reload
Batch	run_sweep	Parallel parameter sweeps via ProcessPoolExecutor
Export	export_csv plot_data export_state_log plot_trajectory	Save trajectories to CSV and plot
Media (optional)	export_video	Render trajectory as MP4 or GIF video
Spatial	scene_map body_aabb surface_anchor compute_placement	AABB, surface anchors, placement computation
Menagerie	list_menagerie_models validate_menagerie_model load_menagerie_model	Download and load 50+ robots from MuJoCo Menagerie
Control	create_controller plan_trajectory step_controller get_controller_state	PID + min-jerk trajectory for arms/quadrupeds/humanoids
Sensor Fusion	configure_sensor_fusion get_fused_state	Low-pass filtered joint state estimation
Coordination	coordinator_add_robot coordinator_get_status coordinator_check_collisions coordinator_assign_task	Multi-robot fleet management
RL	create_rl_env rl_step	Gymnasium-compatible RL environment wrapper
Viewer	viewer_open viewer_sync viewer_close	Live interactive viewer (requires display)
Vision (optional)	analyze_scene compare_scenes track_object render_figure_strip	Gemini 2.5 Pro scene analysis and trajectory tracking
Kinematics	solve_ik	Damped Least Squares IK for end-effector sites
Optimization	optimize_ilqr optimize_mppi	iLQR and MPPI trajectory optimization
Robustness	apply_perturbation stability_analysis randomize_dynamics	Perturbation robustness analysis and domain randomization
Diagnostics	validate_mjcf model_summary suggest_contact_params diagnose_instability	Pre-load XML validation, model overview, contact tuning, instability detection
Workflow	run_and_analyze debug_contacts evaluate_trajectory compare_trajectories	Composite research workflows
Meta	server_diagnostics	Server health, GL backend, and loaded slots

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Optional Extras

Video Export

pip install -e ".[media]"

Enables export_video — render any recorded trajectory as MP4 (requires imageio[ffmpeg]) or GIF (Pillow only, no extra deps).

Vision Analysis

pip install -e ".[vision]"
export GEMINI_API_KEY=your_key

Enables analyze_scene, compare_scenes, track_object, and render_figure_strip via Gemini 2.5 Pro.

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Environment Variables

Variable	Default	Description
MUJOCO_GL	auto-probed	GL backend: egl (GPU) or osmesa (CPU headless)
MUJOCO_MCP_RENDER_WIDTH	640	Render width in pixels
MUJOCO_MCP_RENDER_HEIGHT	480	Render height in pixels
MUJOCO_MCP_NO_RENDER	0	Set 1 to skip GL init entirely
MUJOCO_MCP_MAX_WORKERS	8	Worker processes for run_sweep
GEMINI_API_KEY	—	Required for Vision tools
GEMINI_VISION_MODEL	gemini-2.5-pro	Override Gemini model

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Architecture

src/mujoco_mcp/
├── server.py           # FastMCP app + lifespan context
├── sim_manager.py      # Multi-slot simulation manager (thread-safe)
├── _registry.py        # FastMCP instance (avoids circular imports)
├── constants.py        # Shared constants (yield intervals, thresholds)
├── tools/              # All @mcp.tool() registrations (one file per group)
├── resources.py        # MCP Resources
├── prompts.py          # MCP Prompts (7 workflow prompts)
└── utils/
    └── gl_setup.py     # Probe EGL → OSMesa; sets MUJOCO_GL before mujoco import

Key patterns:

• Each tool group has a _XXX_impl() sync function (for tests) + async MCP wrapper
• @mcp.tool() outer decorator, @safe_tool inner — all exceptions return JSON errors, never raise
• SimManager holds named slots ("default", "exp1", …), each with model, data, trajectory, and optional controller/RL env

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Development

# Run all tests
pytest tests/

# Single file
pytest tests/test_sim_tools.py -v

# Lint
ruff check src/mujoco_mcp

Tests require OSMesa for headless rendering (conftest.py sets MUJOCO_GL=osmesa automatically).

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Contributing

Contributions are welcome. A few guidelines:

• New tool group: create src/mujoco_mcp/tools/<group>.py, register in server.py, add tests in tests/test_<group>.py
• _impl pattern: keep a synchronous _XXX_impl() function for direct test invocation; the async MCP wrapper is a thin shell
• Tests first: write failing tests before implementing
• No new hard dependencies without discussion — optional extras go in pyproject.toml [project.optional-dependencies]

Open an issue before starting large features.

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License

MIT