IDE and client configuration#

You can launch PyCFX-MCP from any MCP-compatible client that supports STDIO or Streamable HTTP servers. Use STDIO for local IDE integrations because the client owns the server process lifecycle.

Claude Code#

For Claude Code, register the STDIO server with the package command:

claude mcp add --transport stdio pycfx -- ansys-cfx-mcp

To run directly from the source repository with uvx:

claude mcp add --transport stdio pycfx -- uvx --from git+https://github.com/ansys/pycfx-mcp ansys-cfx-mcp

For a local Windows checkout, use the virtual environment entry point:

claude mcp add --transport stdio pycfx -- .venv/Scripts/ansys-cfx-mcp.exe

Visual Studio Code#

For Visual Studio Code MCP support, add PyCFX-MCP to the .vscode/mcp.json file. For an installed package, use the console command:

{
   "servers": {
      "pycfx": {
         "type": "stdio",
         "command": "ansys-cfx-mcp"
      }
   }
}

For a local Windows checkout, point Visual Studio Code to the virtual environment entry point:

{
   "servers": {
      "pycfx": {
         "type": "stdio",
         "command": ".venv/Scripts/ansys-cfx-mcp.exe"
      }
   }
}

Claude Desktop#

For Claude Desktop, add the server under mcpServers in the Claude Desktop configuration file:

{
   "mcpServers": {
      "pycfx": {
         "command": "ansys-cfx-mcp"
      }
   }
}

For a local Windows checkout, use the virtual environment executable path:

{
   "mcpServers": {
      "pycfx": {
         "command": ".venv/Scripts/ansys-cfx-mcp.exe"
      }
   }
}

Cursor#

Cursor supports MCP server entries similar to Visual Studio Code. Add PyCFX-MCP as a STDIO server:

{
   "mcpServers": {
      "pycfx": {
         "command": "ansys-cfx-mcp"
      }
   }
}

For a local checkout, set command to the virtual environment executable and then restart Cursor.

Environment variables#

Client-launched STDIO servers inherit the environment from the client process. Set provider keys and LLM routing variables before you launch the client when you want optional codegen LLM fallback.

Common environment variables#

Variable

Description

OPENAI_API_KEY

API key for native OpenAI model calls through the providers extra.

ANTHROPIC_API_KEY

API key for native Anthropic Claude model calls through the providers extra.

GEMINI_API_KEY or GOOGLE_API_KEY

API key for native Google Gemini model calls through the providers extra.

AZURE_API_KEY

API key for Azure OpenAI model calls through the providers extra.

LLM_PROVIDER

Explicit provider name, such as openai, azure, anthropic, gemini, or compat.

LLM_MODEL

Model identifier, such as gpt-4o, claude-3-5-sonnet, or gemini-1.5-pro. The default is gpt-4o-mini.

LLM_ENDPOINT

OpenAI-compatible endpoint URL for direct HTTP transport.

LLM_API_KEY

API key for LLM_ENDPOINT direct HTTP transport.

For the full reference, see Configuration.

HTTP transport#

Use STDIO for most local client integrations. For Streamable HTTP transport, start PyCFX-MCP yourself and configure your client to use the local endpoint:

ansys-cfx-mcp --transport http --host 127.0.0.1 --port 8000

Warning

HTTP transport is intended for trusted local integrations. Put it behind authentication and TLS before using it across a network boundary.

Toolset discovery#

PyCFX-MCP exposes a toolsets://definition resource for clients or conductors that discover related tool groups. The default CFX toolsets group connection management, CFX workflow routing, CFX model context, code generation, and code execution.

Next steps#