uam-analyst
Click on "Deploy 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., "@uam-analystSimulate UAM operations at downtown hub with default config"
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.
KRA33-PRISMX UAM Vertiport Simulation
A discrete-event simulation platform for Urban Air Mobility (UAM) vertiport networks — models eVTOL dispatch, charging, and KPI collection, with a Streamlit dashboard, an MCP server for AI-assisted analysis, and a genetic-algorithm optimizer for vertiport network layout.
Components
File | Role |
| Core simulation engine — |
| Streamlit dashboard for running simulations and visualizing results (maps, charts). |
| MCP server exposing the simulator as tools for Claude Desktop ("Strategic Operations Analyst" — run simulations, analyze KPIs, what-if studies). |
| Detached background process for long-running simulation/sensitivity jobs, spawned by the MCP server. |
| LAUSA — Latent-factor Analysis of Urban Suitability & Attractiveness, a pre-simulation site-selection module for scoring candidate vertiport locations. |
| Single packaged entry point (dispatches to dashboard / |
| C++ hybrid genetic algorithm for vertiport network optimization. |
Related MCP server: vlp-mcp-agent
Requirements
Python 3.12
gurobipy(requires a Gurobi license for problem sizes beyond the trial limit)A C++ compiler (for building
C_HGA/uam_hybrid_ga.cpp, Windows/MinGWMakefileincluded)
pip install -r requirements.txtRunning
Dashboard (dev):
streamlit run uam_web_app.pyMCP server (for Claude Desktop):
python uam_mcp_analyst.pyAdd it to Claude Desktop's MCP config (claude_desktop_config.json — accessible via Claude Desktop → Settings → Developer → Edit Config), then restart Claude Desktop.
Running from source (requires Python + dependencies installed):
{
"mcpServers": {
"uam-analyst": {
"command": "python",
"args": ["<absolute-path-to>/uam_mcp_analyst.py"]
}
}
}Replace <absolute-path-to> with the full path to uam_mcp_analyst.py on your machine.
Running the packaged .exe (no Python needed — see Building the standalone executable):
{
"mcpServers": {
"uam-analyst": {
"command": "<absolute-path-to>/UAMSimulator.exe",
"args": ["--mcp-server"]
}
}
}Replace <absolute-path-to> with the full path to UAMSimulator.exe (e.g. dist/UAMSimulator/UAMSimulator.exe after building, or wherever you extracted the release zip).
Packaged entry point (same three modes, used by the built .exe):
python uam_app.py # dashboard
python uam_app.py --mcp-server # MCP server
python uam_app.py --worker <task_type> <token> <json> # background jobBuilding the standalone executable
pyinstaller UAMSimulator.specOutput goes to dist/UAMSimulator/. The built app is distributed via GitHub Releases rather than committed to the repo.
Data
Sample datasets live in sample_data/ (demand data, vertiport info, LAUSA site-scoring geojson). simulation_config.json holds default simulation parameters.
This server cannot be deployed
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