Tools and capabilities#

You can use these tools from the default MCP surface:

Tool	Purpose
`session_status`	Inspect the active CFX session state.
`connect`	Connect to CFX resources.
`disconnect`	Release active sessions.
`cfx_workflow`	Coordinate lifecycle actions such as CFX-Pre, CFX-solver, and CFD-Post workflows.
`cfx_model_context`	Retrieve bounded model context, API help, named objects, and state snippets.
`codegen`	Generate CFX workflow code snippets. This tool uses deterministic recipes first and can optionally use a server-side LLM fallback.
`clarify`	Ask for missing details before generating or running a workflow.
`run_code`	Run validated Python code in the configured backend.
`validate_code`	Pre-check Python code before execution.

Recommended tool order#

Use the tools in this order for most workflows:

Run session_status to see whether a backend is connected.
Run connect or cfx_workflow with start_pre when you need a CFX-Pre session.
Run cfx_model_context to inspect named objects, allowed values, or a selected state.
Run cfx_workflow for lifecycle operations such as importing meshes, writing solver input, running the solver, and opening results.
Use codegen and validate_code only when your task requires custom PyCFX code.
Run run_code only after validation and user approval in your MCP client.

Optional language model fallback#

Server-side LLM access is optional and is used only by codegen. When a codegen prompt does not match a deterministic CFX recipe, the tool can use the native LiteLLM SDK transport or a direct OpenAI-compatible chat completions endpoint. Install ansys-cfx-mcp[providers] to enable native provider calls through LiteLLM. For direct endpoint HTTP, LLM_ENDPOINT can be a full /chat/completions URL or a base URL such as a LiteLLM proxy’s http://localhost:4000/v1.

$env:LLM_ENDPOINT = "http://localhost:4000/v1"
$env:LLM_API_KEY = "<token>"
$env:LLM_MODEL = "gpt-4o-mini"

LLM_TRANSPORT defaults to auto. When LLM_ENDPOINT is set, auto uses direct endpoint HTTP. Otherwise, provider keys such as OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, or AZURE_API_KEY enable the native LiteLLM transport. Set LLM_AUTH_STYLE=azure-api-key for Azure OpenAI endpoints that require an api-key header.

cfx_workflow, cfx_model_context, validate_code, and run_code do not call a server-side LLM.

Common workflow actions#

Action	Example request
`start_pre`	Start CFX-Pre to prepare a setup.
`import_mesh`	Import this mesh file into the active CFX-Pre session.
`write_def`	Write a solver input file for the current setup.
`start_solver`	Start the solver for this solver input file.
`wait_solver`	Wait until the current solver run finishes.
`get_results_file`	Return the result file produced by the solver run.
`open_post`	Open this result file in CFD-Post.

Context inspection actions#

Ask for targeted context instead of broad dumps. For example:

Find named boundary objects that contain inlet.
Show allowed values for a selected boundary setting.
Return API help for a specific CFX path.
Fetch state only for these paths and named-object types.