Overview#

What is PyFluent-MCP?#

Use PyFluent-MCP (ansys.fluent.mcp) as a bridge between external MCP clients and Ansys Fluent. It uses the Model Context Protocol (MCP) to expose PyFluent capabilities as standardized deterministic tools.

What is MCP?#

MCP is a standardized interface for connecting external clients to tools and data sources. It allows clients to perform the following tasks:

  • Discover available tools and their capabilities.

  • Call tools with structured parameters.

  • Receive results and error information.

  • Maintain state across multiple interactions.

How does MCP work?#

  • Client connection: An MCP-compatible client connects to the PyFluent-MCP server.

  • Tool discovery: The client discovers available tools for controlling Fluent.

  • Tool execution: The client calls tools with appropriate parameters.

  • Result return: The server returns results or errors to the client.

  • Interaction loop: The cycle continues for the duration of the session.

Understand the architecture#

PyFluent-MCP includes several key components under the ansys.fluent.mcp namespace:

  • MCP server (SolveMCP): Implements the MCP protocol and handles client connections.

  • Tool surface: Stateless tools for connection, discovery, execution, validation, and reporting.

  • PyFluent backend: In-process gRPC to a local or remote Fluent solver.

  • Settings catalog: Offline schema (~62k paths) with local BM25-based search.

  • AST sandbox: Validates Python before it reaches the solver.

PyFluent-MCP intentionally stays a deterministic substrate. It does not own provider orchestration, runtime model routing, transport policy, retries, workflow reasoning, or agent loops. Those concerns belong in higher-level products or external MCP hosts that consume this package over MCP.

Design tenets#

  • Stateless per call. Each tool call is self-contained except for the live Fluent connection and the run_code REPL namespace.

  • Python never writes Fluent directly without validation. Python only runs through run_code / validate_code, which pass through an AST sandbox.

  • Offline-first knowledge. find_api and get_help work from the bundled settings schema without any network service.

  • Pluggable backends. PyFluent ships in-box. Other backends can be contributed via entry points.

Explore use cases#

Solver setup

Configure boundary conditions, materials, and numerics interactively.

Interactive analysis

Inspect residuals, mesh quality, or setup summaries.

Case comparison

Diff two case files to see what changed between versions.

Learning tool

Use deterministic tooling to learn Fluent’s settings API and PyFluent.

Next steps#