Quick start#

Launch PyMAPDL-MCP#

This command is the simplest way to start the MCP server:

ansys-mapdl-mcp

It launches the server and waits for connections from MCP clients.

Connect to your IDE or client#

PyMAPDL-MCP works with multiple MCP-compatible clients. For setup information, see IDE and client configuration.

  • Claude Code (recommended for AI-assisted development)

  • Visual Studio Code with Copilot (for Visual Studio Code users)

  • Claude Desktop (macOS app)

  • Other MCP-compatible clients

Follow the basic workflow#

Start a MAPDL instance#

There are three ways to connect to MAPDL once the MCP server is running.

Option 1: Launch a new MAPDL instance (recommended).

Ask your AI assistant to use the launch_mapdl_session tool:

“Launch a new MAPDL instance.”

“Launch MAPDL with 4 processors in /tmp/mapdl_work.”

This starts a new MAPDL process and connects to it automatically. It lets you specify custom settings (number of processors, working directory, etc.) without any manual setup.

Option 2: Connect to an existing instance.

Ask your AI assistant to use the connect_to_mapdl tool:

“Connect to MAPDL on localhost port 50052.”

“Connect to MAPDL on 192.168.1.100 port 50053.”

You can first ask it to run list_mapdl_instances to discover what is running. This option is useful for connecting to different instances during a session or when MAPDL is already running on a remote machine.

Option 3: Auto-connect on server startup.

Pass --connect-on-startup when starting the MCP server:

python -m ansys.mapdl.mcp --connect-on-startup --ip 127.0.0.1 --port 50052

Warning

When --connect-on-startup is used, the connection is locked. The launch_mapdl_session, connect_to_mapdl, and disconnect_from_mapdl tools are turned off.

Running commands and extracting results#

Once MAPDL is connected, you can use MCP tools to perform these tasks:

  1. Launch MAPDL instances.

  2. Execute MAPDL commands through PyMAPDL.

  3. Retrieve and analyze results.

  4. Generate plots and screenshots.

  5. Manage the MAPDL session lifecycle.

Consider example use cases#

  • Run parametric studies with AI guidance.

  • Analyze FEA results automatically.

  • Generate documentation from simulation results.

  • Debug MAPDL scripts with AI assistance.

Next steps#