maya-mcp-server 0.1.0
MCP server for interacting with Autodesk Maya sessions
Requires Python: >=3.10
Keywords: 3d, ai, animation, autodesk, maya, mcp

# maya-mcp-server

MCP server for interacting with Autodesk Maya sessions.

## Features

- **Multi-session support**: Manage multiple Maya sessions from a single MCP server. The server scans for new Maya sessions that have been started and shutdown by the user.
- **Full Python expressiveness**: Execute arbitrary Python code. Agents can create virtual python modules to expose functions for execution, including by the user.
- **Streaming output**: Capture stdout/stderr from Maya sessions via MCP resources. Agents can monitor output from their code or user activity.
- **Zero Maya-side setup**: Leverages Maya's default command port
- **Easy installation**: Install and run via `uvx maya-mcp-server`

![screen_recording_4x.gif](screen_recording_4x.gif)

## Installation

```bash
# Using uvx (recommended)
uvx maya-mcp-server

# Or install with pip
pip install maya-mcp-server
```

## Usage

### Claude Code Configuration

Add to your Claude Code MCP configuration, run:

```commandline
claude mcp add --transport stdio maya -- uvx maya-mcp-server
```

The default scope is "local", which adds it to your `~/.claude.json` keyed to a particular project directory.  Setting `--scope=user` adds to `~/.claude.json` across all projects, and `--scope=project` to add the configuration into a `.mcp.json` in the current project directory, so that it can be commited to your repo.

For local development use: 
```commandline
claude mcp add --transport stdio maya -- uv run --directory /path/to/maya-mcp-server/ maya-mcp-server
```

### Maya Setup

The server automatically discovers Maya sessions via command ports. To enable a Python command port in Maya:

```python
import maya.cmds as cmds
cmds.commandPort(name=":7002", sourceType="python")
```

Or add to your `userSetup.py` for automatic startup.

## Tools

Tools accept an optional `session_key` parameter for targeting specific sessions.
If only one Maya session exists, it will be auto-selected.
Session keys are returned by `list_sessions` and `add_session`.

| Tool | Description |
|------|-------------|
| `list_sessions` | List all active Maya sessions. Returns session info including `session_key` for use with other tools/resources. |
| `add_session` | Manually add a Maya session at a specific host:port. Use when auto-discovery doesn't find your session. |
| `write_module` | Create a virtual Python module in Maya. Useful for defining reusable functions. |
| `execute_code` | Execute Python code in a session. Supports result capture modes: `NONE`, `JSON`, `RAW`. |

## Resources

| Resource | Description |
|----------|-------------|
| `maya://sessions/{session_key}/info` | Session information (pid, user, maya_version, scene_name, scene_path) |
| `maya://sessions/{session_key}/output` | Captured stdout/stderr output from the session |

## Similar tools

### [MayaMCP](https://github.com/PatrickPalmer/MayaMCP/)

This looks to be the first publicly available MCP server for Maya and I was inspired by a few aspects of this tool, especially the goal of zero Maya-side setup.

Disadvantages:
* The MCP server is bound to a single Maya session running on the default port.
* It is limited to a bespoke set of tools.  This could be seen as a security advantage, but it cripples the ability of an agent to do just about anything.
* No support for reading stdout or stderr, so the agent is blind to what's happening in the Maya session.
* Less robust approach to capturing command output (e.g. does not check if code is indented within a `for` loop or function)
* Can't run via `uvx`, or `pip install` from pypi.

### [ChatGPT4Maya](https://github.com/thejoltjoker/ChatGPTforMaya)

This is the original LLM integration for Maya, which embeds ChatGPT directly in a PySide window and enables the LLM to respond to user commands and queries by executing code in the session.

Disadvantages:
* Not an MCP server, so it cannot take full advantage of agentic workflows.
* Only works with ChatGPT.

### [Jupyter MCP Server](https://jupyter-mcp-server.datalayer.tech/)

This provided an interesting reference for how to create an MCP server in python that works with multiple remote sessions (in this case, notebooks) to execute arbitrary code.

## Development

```bash
# Install dev dependencies
uv sync --dev

# Run tests
uv run pytest

# Run the server
uv run maya-mcp-server
```

## License

MIT

## TODO

- [ ] Provide an option to `execute` to run in global or private context.
- [ ] Yield output as it's printed?
- [ ] Add tools to simplify interaction with UI: shelves, hotkeys, menus
- [ ] Plugins to extend session info, e.g. with custom pipeline info
- [ ] Investigate RPC for extensibility, implementation of custom tools
- [ ] Use a dispatch function for command port mode, to further harmonize. Create a shared type safe collection of tools that hold name and arguments. 
- [ ] Return stdout and stderr lines interleaved (and prefixed with `STDOUT:` `STDERR:`) so that the agent can determine order?
- [ ] Cleanup command ports when complete.  This won't be necessary if we default to the Qt command server.