CFAST
FreeNot checkedEnables LLMs to build, inspect, run, and analyze CFAST fire models step by step via tools for compartments, materials, vents, fires, devices, and surface connec
About
Enables LLMs to build, inspect, run, and analyze CFAST fire models step by step via tools for compartments, materials, vents, fires, devices, and surface connections, with simulation and result summaries.
README
CI Status pre-commit.ci status uv Ruff MyPy Checked PyPI - Python Version PyPI codecov License: MIT
CFAST MCP is an MCP server that lets an AI assistant build, run, and analyze CFAST (Consolidated Fire and Smoke Transport, NIST) fire simulations through conversation. It is built on top of PyCFAST and exposes the CFAST model as a set of tools. The AI assistant is able to create a model, add compartments, materials, vents, fires and devices step by step, run CFAST, and make summaries of the results.
Example
Ask your assistant something like:
Create a 4 m × 3 m × 2.5 m room with a door (0.9 × 2 m) to the outside and a fire growing to 1 MW in 300 s. Run it and give me the peak upper-layer temperature then show me the folder where you create the file, so I can inspect it.
Results will probably look like this:
Tools
| Group | Tools |
|---|---|
| Create & configure | create_model, update_simulation |
| Components | add_* / update_* for materials, compartments, wall vents, ceiling/floor vents, mechanical vents, fires, devices (targets & detectors), surface connections |
| Inspect | inspect_model (summary, optional .in file), get_model_files |
| Run & results | run_model, get_results (bounded previews and per-column min/max/final stats) |
Results are returned to the AI assistant as small text summaries. The generated files (.in, output .csv, logs) are written in a temporary directory while the session is active. Use get_model_files to locate them if you want to open them directly.
Note: models live in memory for the lifetime of the server process. Restarting the server (or your MCP client) will delete them.
Installation
Requires Python 3.10+ and CFAST 7.7.0+.
uvx (Recommended)
Install uv, then add cfast-mcp directly in your client configuration:
{
"mcpServers": {
"cfast": {
"command": "uvx",
"args": ["cfast-mcp"],
"env": { "CFAST": "/path/to/your/cfast/executable" }
}
}
}
Claude Code
If you use Claude Code, a single command registers the server:
claude mcp add cfast -e CFAST=/path/to/your/cfast/executable -- cfast-mcp
Pip
Create a virtual environment and install from PyPI:
python -m venv venv
source venv/bin/activate # Linux/macOS
venv\Scripts\activate # Windows
pip install cfast-mcp
Then add cfast-mcp to your client configuration:
{
"mcpServers": {
"cfast": {
"command": "cfast-mcp",
"env": { "CFAST": "/path/to/your/cfast/executable" }
}
}
}
CFAST Installation
Download and install CFAST from the NIST CFAST website or the CFAST GitHub repository. Follow the installation instructions for your operating system and ensure cfast is available in your PATH. If CFAST is installed in a non-standard location, you can manually specify the path by setting the CFAST environment variable to point to the CFAST executable.
export CFAST="/path/to/your/cfast/executable" # Linux/macOS
set CFAST="C:\path\to\cfast.exe" # Windows (cmd)
$env:CFAST="C:\path\to\cfast.exe" # Windows (PowerShell)
Development
git clone https://github.com/bewygs/cfast-mcp.git
cd cfast-mcp
uv sync --extra dev # install dev dependencies
uv run pytest # run tests
uv run ruff check --fix . # lint
uv run mypy src/ # type-check
Install CFAST in Claude Desktop, Claude Code & Cursor
unyly install cfast-mcpInstalls into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.
First time? Get the CLI: curl -fsSL https://unyly.org/install | sh
Or configure manually
Run in your terminal:
claude mcp add cfast-mcp -- uvx cfast-mcpFAQ
Is CFAST MCP free?
Yes, CFAST MCP is free — one-click install via Unyly at no cost.
Does CFAST need an API key?
No, CFAST runs without API keys or environment variables.
Is CFAST hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install CFAST in Claude Desktop, Claude Code or Cursor?
Open CFAST on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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