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fluent-mcp-server

by jiweiqi
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Python
Remote

ANSYS Fluent MCP Server

A Model Context Protocol (MCP) server that helps AI assistants navigate ANSYS Fluent online documentation efficiently.

Features

  • Smart URL Navigator - Find relevant ANSYS Help documentation URLs instantly

  • 35+ Topic Routes - Pre-mapped paths to common Fluent topics

  • Always Up-to-Date - Points to official ANSYS documentation

  • Zero Maintenance - No static content to update

  • Lightweight - Fast URL generation, no heavy indexing

Related MCP server: ANSYS MCP Server

Quick Start

Prerequisites

  • Python 3.10 or higher

  • uv package manager

Installation

Choose your MCP client below for setup instructions:

claude mcp add fluent -- uvx fluent-mcp-server

Verify:

claude mcp list
claude mcp info fluent

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "fluent": {
      "command": "uvx",
      "args": ["fluent-mcp-server"]
    }
  }
}

Restart Claude Desktop completely after saving.

Go to Cursor Settings → MCP → Add new MCP Server:

  • Name: fluent

  • Command: uvx fluent-mcp-server

Or edit config file:

macOS: ~/Library/Application Support/Cursor/mcp_config.json Windows: %APPDATA%\Cursor\mcp_config.json Linux: ~/.config/Cursor/mcp_config.json

{
  "mcpServers": {
    "fluent": {
      "command": "uvx",
      "args": ["fluent-mcp-server"]
    }
  }
}

CLI:

code --add-mcp '{"name":"fluent","command":"uvx","args":["fluent-mcp-server"]}'

Or add to VS Code settings:

{
  "github.copilot.chat.mcp.servers": {
    "fluent": {
      "command": "uvx",
      "args": ["fluent-mcp-server"]
    }
  }
}

Install Cline extension, then add to VS Code settings:

{
  "cline.mcpServers": {
    "fluent": {
      "command": "uvx",
      "args": ["fluent-mcp-server"]
    }
  }
}

Reload VS Code window after saving.

For 11 additional clients (Windsurf, Continue, Amp, Codex, Gemini CLI, Goose, Kiro, LM Studio, opencode, Qodo Gen, Warp), see Complete Setup Guide →

Local Development

# Clone the repository
git clone https://github.com/your-org/fluent-mcp-server.git
cd fluent-mcp-server

# Install with uv
uv pip install -e ".[dev]"

# Run tests
uv run pytest

# Run server
uv run fluent-mcp-server

Development configuration (.mcp.json in project root):

{
  "mcpServers": {
    "fluent": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/fluent-mcp-server",
        "run",
        "fluent-mcp-server"
      ]
    }
  }
}

Available Tools

search_help

Find ANSYS Fluent documentation URLs for your query. Returns search URLs and suggested manual sections.

Parameters:

  • query (required): Search term (e.g., "flamelet model", "read case", "turbulence")

  • max_suggestions (optional): Maximum manual section suggestions (default: 3)

Returns:

  • ANSYS Help search URL

  • Suggested manual sections (if topic is pre-mapped)

  • Links to all major manuals

Example:

search_help("flamelet model")
# Returns:
# - Search URL: https://ansyshelp.ansys.com/search?q=flamelet+model
# - Suggested: flamelet User Guide + Theory Guide
# - All manuals: User Guide, TUI, Theory, UDF

search_help("read case")
# Returns URLs to file/read-case TUI documentation

list_topics

List the 35+ topics with pre-mapped documentation routes.

Returns: List of topics like: file, turbulence, combustion, flamelet, mesh, boundary, udf, etc.

Example:

list_topics()

get_manual_link

Get direct link to a specific Fluent manual or section.

Parameters:

  • manual (required): Manual name (user_guide, tui, theory, udf)

  • section (optional): Section path (e.g., "turbulence", "file/read-case")

Example:

get_manual_link("udf", "introduction")
# Returns: https://ansyshelp.ansys.com/.../flu_udf/introduction.html

Usage Examples

In Claude Code / AI Assistant

You: How do I use the flamelet model in Fluent?
Claude: [Calls search_help("flamelet model")]
        [Receives URLs to flamelet documentation]
        [Uses WebFetch to read the actual documentation]
        [Provides answer based on official ANSYS docs]

You: Show me the command to read a case file
Claude: [Calls search_help("read case")]
        [Gets URL to file/read-case TUI command]
        [Retrieves documentation and shows syntax]

Architecture Flow

User Query → MCP search_help → Returns URLs →
LLM uses WebFetch → Reads official docs → Answers user

The MCP server acts as a smart navigator, not a content database.

Architecture

fluent-mcp-server/
├── src/fluent_mcp_server/
│   ├── server.py          # FastMCP server & tool definitions
│   ├── doc_finder.py      # Smart URL router (35+ topic mappings)
│   └── __init__.py
├── md-files/
│   ├── ARCHITECTURE.md    # Technical architecture
│   └── ...
├── tests/                 # Unit tests
├── examples/              # Usage examples
└── pyproject.toml        # Project configuration

Roadmap

Phase 1.x: Route Expansion (Future)

  • Add more topic routes based on usage patterns

  • Version selection (v251, v252, v253)

  • Usage analytics to identify popular topics

Phase 2: PyFluent Integration (Planned)

  • Connect to Fluent sessions

  • Execute TUI commands programmatically

  • Case I/O via PyFluent API

  • Mesh quality automation

  • Convergence monitoring

Phase 3: Workflow Automation (Planned)

  • Boundary condition templates

  • Solver configuration wizards

  • Post-processing automation

  • Parametric studies

See md-files/ARCHITECTURE.md for detailed roadmap.

Documentation

Getting Started:

Technical Documentation:

Validation & Analysis:

Development:

License

MIT License

Contributing

Contributions welcome! Please ensure:

  • Tests pass: uv run pytest

  • Code follows existing style

  • Documentation updated

fluent-mcp-server

Available Tools

3 tools
list_topicsA

List common Fluent topics with quick documentation links.

Returns: List of pre-mapped topics for faster navigation

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It mentions the tool returns a list of pre-mapped topics with links, but does not disclose any limitations, ordering, or whether the list is static/dynamic. Adequate but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise with two short sentences plus a returns line. Front-loaded with the verb 'List' and no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and an output schema (as per context), the description adequately covers the purpose. However, it could be slightly improved by clarifying what 'common' or 'pre-mapped' means in this context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so baseline is 4. The description adds no parameter information, which is acceptable as there is nothing to document.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists common Fluent topics with documentation links, using a specific verb and resource. It is distinguishable from siblings 'get_manual_link' and 'search_help' which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus its siblings or other alternatives. The description does not include any when-to-use or when-not-to-use context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_helpA

Find ANSYS Fluent documentation URLs for your query.

This tool helps you navigate to the right online documentation. Use WebFetch to retrieve the actual content from the returned URLs.

Args: query: Search term (e.g., "flamelet model", "read case", "turbulence") max_suggestions: Maximum manual section suggestions to return (default: 3)

Returns: URLs and navigation hints for finding the documentation

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
max_suggestionsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes what the tool returns (URLs and navigation hints) and what is not done (actual content retrieval). No hidden behaviors are implied. Since annotations are absent, the description carries the burden and does well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, front-loaded description with no unnecessary words. Clear separation of purpose, usage, and parameter details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Provides enough context for an agent to understand the tool's role, including the need for WebFetch and the nature of the return. Output schema is not shown but mentioned in description; completeness is adequate for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds examples for the 'query' parameter and explains the purpose of 'max_suggestions' with default value. Though schema coverage is 0%, the description compensates by providing practical guidance beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it finds ANSYS Fluent documentation URLs. Example queries and the specific parameter descriptions reinforce the purpose. Differentiates from siblings by being a search tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Advises using WebFetch to retrieve content from URLs, which is a useful follow-up step. However, it does not explicitly state when to use this tool over siblings like get_manual_link or list_topics.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv0.2.0
    • First observedget_manual_link
    • First observedlist_topics
    • First observedsearch_help
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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose: get_manual_link fetches a direct link to a specific manual section, list_topics provides pre-mapped common topics, and search_help performs general searches. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: get_manual_link, list_topics, search_help. The naming is predictable and readable.

Tool Count4/5

With 3 tools, the set is compact but covers the essential operations for Fluent documentation access: direct linking, topic listing, and search. This count is appropriate for a focused server, though slightly lower than typical ranges.

Completeness4/5

The tools cover the main use cases for finding documentation: specific manual links, common topics, and free-text search. Minor gaps like browsing by category or full table of contents are acceptable for the scope.

Maintenance

ActivityInactive
ResponsivenessSyncing

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