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Ansys Fluent Mcp

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Cai-aa
ansys-fluent-mcp

Drive an ANSYS Fluent solver session through the standalone `ansys-fluent-mcp` MCP server. USE WHEN the user asks to load a case/mesh, inspect physics, change settings, run iterations, or query results — and the chat client is connected directly to the MCP (VS Code Copilot, Cursor, Claude Desktop). EXPOSES a 22-tool surface: `session_status`, `connect`, `disconnect`, `find_api`, `get_help`, `get_state`, `get_targeted_context`, `list_named_objects`, `find_named_object`, `select_named_objects`, `summarize_setup`, `simulation_report`, `solver_status`, `run_code`, `validate_code`, `screenshot`, `mesh_quality`, `list_fields`, `compare_files`. This is a STATELESS leaf: there is no plan/journal/rollback and no `propose_step` / `finalize` planning machinery — `run_code` mutates the live solver immediately. DO NOT USE FOR generic Python / CFD-theory questions unrelated to Fluent.

Overview

PublisherCai-aa
RepositoryCAE-Agent-Hub
Skill nameansys-fluent-mcp
Stars
1K
Forks
130
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Cai-aa on GitHub. Read the source before you install it.

Installation

Install the Ansys Fluent Mcp AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/MCP/Ansys/PyAnsys/pyfluent-mcp/src/ansys/fluent/mcp/solve/skills
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Cai-aa/CAE-Agent-Hub.git /tmp/CAE-Agent-Hub
mkdir -p .claude/skills
cp -r /tmp/CAE-Agent-Hub/MCP/Ansys/PyAnsys/pyfluent-mcp/src/ansys/fluent/mcp/solve/skills .claude/skills/ansys-fluent-mcp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ansys Fluent Mcp in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ansys Fluent Mcp on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ansys Fluent Mcp is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ANSYS Fluent MCP (standalone) — Authoring Playbook

This skill targets the tool surface that ansys-fluent-mcp ships. The server gives you composable primitives — discovery, state read, schema discovery, validation, sandboxed execution — and you build everything else by chaining them. It is stateless: each call stands alone, there is no plan builder, journal, or undo.

Tool roster (this is what you actually have)

ToolOne-line purpose
session_statusAre we connected? Which backend?
connect(ip?, port?, ...)Launch a local Fluent session or attach to a running one.
disconnectTear down the session.
solver_statusIs the solver busy iterating?
find_api(query, limit?, kind?)Lexical (or, if configured, semantic) search over the Fluent settings tree. Returns ranked paths + signatures.
get_help(path)Per-node help text.
get_state(path, projection?)Read any settings path. path is a STRING.
get_targeted_context(...)Trimmed live-context snapshot for a specific intent.
list_named_objects(path)Enumerate the keys of a NamedObject collection.
find_named_object(path, name, fuzzy?)Exact or fuzzy name lookup.
select_named_objects(path, where=[...])Filter a NamedObject collection by predicates.
summarize_setup(scope?)Compact digest of models / BCs / materials / numerics / reference values.
simulation_reportStructured solution report on the loaded case.
run_code(code)Execute a sandboxed PyFluent Python snippet. This is your mutator.
validate_code(code)AST + signature check (no execution). Always run before run_code for untrusted code.
screenshot(filename?, width?, height?)Capture the active graphics window.
mesh_quality(include_check?)Live skewness / orthogonal-quality / aspect-ratio histograms (+ optional mesh.check()). Route every "show mesh quality" / "skewness" / "orthogonal quality" / "aspect ratio" / "check mesh" intent here.
list_fields(scope?)Enumerate scalar/vector fields available in the loaded case (pressure, velocity-magnitude, …).
compare_files(path_a, path_b)Diff two case/mesh files in two ephemeral PyFluent sessions; returns a markdown summary table per family. Live workspace session is not touched.

That is the complete surface. There is no propose_step, finalize, summarize_session, inspect_api, describe_command, get_allowed_values, query_reports, validate_setup, diagnose_divergence, or engineering-correlation helper (compute_htc, lookup_emissivity, …) on this server. Don't call them — they don't exist here.

The composition pattern

For nearly every intent, the loop is:

1. Discover    → find_api / get_help / list_named_objects / summarize_setup
2. Author      → your host or user supplies PyFluent settings-API Python
3. Validate    → validate_code (offline AST check)
4. Execute     → run_code (mutates the live solver)
5. Verify      → get_state / summarize_setup / simulation_report

Intent-to-code translation is not part of this leaf. If a host wants to translate user intent into Python, it should do so outside this server, then pass the resulting Python through validate_code before calling run_code.

Path roots (universal Fluent knowledge)

PyFluent has exactly five top-level path roots:

setup. | solution. | results. | file. | mesh.

setup.solution.* does not exist.

Recipes for common intents

"Show mesh quality / skewness / orthogonal quality / aspect ratio / check mesh"

Use the mesh_quality tool — it returns structured skewness / orthogonal-quality / aspect-ratio histograms and, when include_check=true, also embeds the output of Fluent's mesh.check():

mesh_quality(include_check=true)

Do NOT route these intents through run_code, simulation_report, or summarize_setup — the dedicated tool already wraps the raw PyFluent calls and normalises their output across versions.

"What fields are available?" / "list scalar fields"

list_fields(scope="any")     # any | cell | node | face

Returns the flat list usable in report defs, contours, vectors.

"Compare two case files"

compare_files(path_a="/abs/path/A.cas.h5", path_b="/abs/path/B.cas.h5")

Both files open in separate ephemeral PyFluent sessions with lightweight_setup=true — your live workspace session is NOT touched. Returns a markdown summary table in the summary field; echo that string verbatim to the user.

"Compute h for natural convection?" / "Porous resistance?" / "Emissivity / roughness of X?"

These engineering correlations and material lookups are not on this server. Ground the value yourself (textbook / handbook) and use it in host-authored Python rather than calling a non-existent tool.

"Show me the current setup"

summarize_setup(scope='models') or summarize_setup(scope='bcs') — don't dump the whole tree. Scoped output keeps client context small and the response on-topic.

"Enable energy" / "set BC outlet to pressure-outlet" / "switch turbulence to k-ω SST"

Author PyFluent settings-API Python, validate it with validate_code, then execute it with run_code.

Always show the generated code to the user before run_code — it mutates the solver immediately and there is no undo on this server.

"What's the path for ?"

find_api(query="velocity inlet thermal temperature", kind="path")

Pick the most-specific match and then:

get_help(path="setup.boundary_conditions.velocity_inlet.<zone>.thermal.t")

"List my zones / BCs / materials"

list_named_objects(path="setup.boundary_conditions.velocity_inlet")
list_named_objects(path="setup.cell_zone_conditions.fluid")
list_named_objects(path="setup.materials.fluid")

For predicate filtering, use select_named_objects with where=[...].

"Read a value"

get_state(path="setup.models.viscous.k_omega_model") — pass a string, not a list. The result is the live value.

"Iterate N steps"

run_code(code="solver.solution.run_calculation.iterate(iter_count=200)")

There's no streaming progress on this surface — you'll get the final residual snapshot. Use solver_status to confirm completion.

"Load a case / mesh"

run_code(code="solver.file.read_case(file_name=r'<path>')")
run_code(code="solver.file.read_data(file_name=r'<path>')")

Golden rules

  • NEVER emit .tui.* calls from run_code. Always solver.settings.* / solver.<root>.<path>... (the settings API is what's available in 27.1).
  • .list() / .list_properties() are void — they print only. Use list_named_objects instead for programmatic discovery.
  • run_code mutates the live solver. There is no journal / rollback on this surface. Show the user the code before you run it.
  • Named expressions REQUIRE units when dimensional: '288.15 [K]', '1.225 [kg/m^3]', '1 [m/s]'. Bare numbers silently break downstream report defs and BC values.
  • get_state(path=...) takes a string, not a list. Same for find_api(query=...).

Anti-patterns (do not do)

  • .tui.* calls of any kind, including via run_code.
  • Calling planning/governance tools (propose_step, finalize, summarize_session, inspect_api, validate_setup) — they are NOT part of this server.
  • Routing "mesh quality" / "skewness" / "orthogonal quality" / "aspect ratio" / "check mesh" through simulation_report, summarize_setup, or hand-written run_code — call the dedicated mesh_quality tool instead (it returns the structured histograms directly and optionally wraps Fluent's mesh.check()).
  • Calling run_code with intent strings (it expects Python, not executable code). Translate intent outside this leaf, validate the resulting Python, then execute it with run_code.
  • Skipping validate_code on host-authored Python for safety-critical operations (model toggles, BC writes, file.write_*).

Planning, retries, and recovery — host responsibility

This server is stateless. It deliberately ships no planner, no journal, no recipe registry, and no rollback. Multi-step plan construction, undo, retry policies, and "propose → confirm → apply" flows are the host's responsibility (VS Code Copilot, Cursor, Claude Desktop, or any custom agent that connects over MCP).

If the host has its own planner / agent loop, let it drive the discover → generate → validate → execute → verify cycle. This server will:

  • accept and run each step,
  • return rich error envelopes the host's planner can branch on,
  • never retry, queue, or sequence steps internally.

Intent-guard error codes (defense-in-depth at run_code)

run_code runs an offline, stateless intent guard before exec to catch a fixed set of Fluent-specific crash signatures. It is NOT a planner — it inspects only the snippet you submitted. Disable with FLUIDS_MCP_INTENT_GUARD=0 if your host wants full control.

When the guard fires, run_code returns one of these error codes:

error_codeMeaningCorrect response
risk_blockedSnippet matches a known crash signature (e.g. boundary rename to a name with whitespace; multiphase.number_of_phases = N int direct-assign; setup mutation while iterating).Read the suggestion in stderr, rewrite the snippet, then call run_code again. Do NOT retry the same snippet.
sequence_errorIntra-snippet use-before-create (e.g. gravity references expr_grav before the snippet creates setup.named_expressions["expr_grav"]).Reorder the statements (define first, then reference) and re-submit.
solver_disconnectedThe Fluent gRPC channel died during the call (or just before it).Call connect again, re-load the case/data file, then re-issue the work. The session is already marked disconnected — session_status will reflect that on the very next call.

The blocking signatures (return risk_blocked / sequence_error) are:

  1. bc.rename.whitespace — boundary rename whose new name contains whitespace. Use underscores instead ("oil_inlet", not "oil inlet").
  2. multiphase.number_of_phases.shape — direct integer assignment to multiphase.number_of_phases. Use multiphase.number_of_phases.number_of_eulerian_phases = N.
  3. seq.phase.material_before_rename — phase rename happens AFTER the material assignment in the same snippet. Rename first, then assign material to the new key.
  4. runtime.write_during_iter — setup.* mutation while the solver is iterating. Interrupt first (solver.solution.run_calculation.interrupt()), then write.
  5. seq.named_expr.use_before_create — a named expression is referenced before it is created in the same snippet. Reorder, or split into two run_code calls.
  6. reportdef.surface_field — a report definition is given a surfaces field (attribute, .locations.surfaces, or a create-dict key). Report definitions take surface_names=[...] (surfaces is the graphics-object field).

In addition, non-blocking advisories are returned in the successful result's warnings list (they do NOT stop execution):

  • named_expr.missing_units — a named expression is created with a bare number ({"definition": "9.81"}). Add a unit, e.g. "9.81 [m s^-2]"; a unit-less number is dimensionless and fails when consumed by a dimensioned slot.
  • tui.usage — the snippet uses .tui.*. Prefer the solver.settings.* tree, which the rest of this server validates against.

These exist purely to prevent today's most expensive failure modes from killing the gRPC channel (blocks) or silently mis-configuring the case (warnings); they do not constitute a planner and they do not cross-call each other.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ansys Fluent Mcp AI skill do?

Drive an ANSYS Fluent solver session through the standalone `ansys-fluent-mcp` MCP server. USE WHEN the user asks to load a case/mesh, inspect physics, change settings, run iterations, or query results — and the chat client is connected directly to the MCP (VS Code Copilot, Cursor, Claude Desktop). EXPOSES a 22-tool surface: `session_status`, `connect`, `disconnect`, `find_api`, `get_help`, `get_state`, `get_targeted_context`, `list_named_objects`, `find_named_object`, `select_named_objects`, `summarize_setup`, `simulation_report`, `solver_status`, `run_code`, `validate_code`, `screenshot`,...

Why use Ansys Fluent Mcp on TypingMind?

Because you install it once and use it with any model. Ansys Fluent Mcp is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ansys Fluent Mcp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Cai-aa/CAE-Agent-Hub/tree/main/MCP/Ansys/PyAnsys/pyfluent-mcp/src/ansys/fluent/mcp/solve/skills. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ansys Fluent Mcp?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ansys Fluent Mcp?

As many as you like. As long as a model supports skills, you can use Ansys Fluent Mcp with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ansys Fluent Mcp AI skill free?

Yes. It is published on GitHub by Cai-aa under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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