CFAST MCP
This server lets an AI assistant build, run, and analyze CFAST (Consolidated Fire and Smoke Transport) fire simulations through conversation.
Model Creation & Configuration
create_model– Create a new simulation with an initial compartment, title, dimensions, duration, and ambient conditionsupdate_simulation– Update simulation-level parameters (title, duration, temperatures, humidity)
Building the Model
add_material/update_material– Define thermophysical surface materials (conductivity, density, specific heat, thickness, emissivity)add_compartment/update_compartment– Add rooms with dimensions, position, and surface material assignmentsadd_wall_vent/update_wall_vent– Add doors, windows, or openings between compartments or to the outsideadd_ceiling_floor_vent/update_ceiling_floor_vent– Add vertical flow openings between stacked compartmentsadd_mechanical_vent/update_mechanical_vent– Add fan-driven ventilation systemsadd_fire/update_fire– Define time-dependent fires with HRR curves, fuel composition, and species yieldsadd_device/update_device– Add targets (PLATE, CYLINDER) or detectors (heat, smoke, sprinkler)add_surface_connection/update_surface_connection– Define heat transfer through solid boundaries between compartments
Inspection & File Access
inspect_model– Get a model summary, optionally including the raw CFAST.ininput fileget_model_files– Locate on-disk files (.in,.csv, logs) for a model
Running & Analyzing Results
run_model– Execute the CFAST simulation with a configurable timeoutget_results– Retrieve bounded previews or per-column statistics (min, max, final value, time of max) from output sets such as compartment temperatures, device readings, vent flows, and diagnostics
Models live in memory during the server session; files are written to a temporary directory.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CFAST MCPCreate a 5x5x3m room with a 100kW fire and run simulation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CFAST MCP
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.
Live demo
A live demo using the MCP server is available on Hugging Face Spaces. You can try it out without any installation:
Related MCP server: BULC Building Designer
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 |
|
Components |
|
Inspect |
|
Run & results |
|
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-mcpPip
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-mcpThen 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-checkMaintenance
Resources
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If you are the server author, to access and configure the admin panel.
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