Skip to main content
Glama
urban-ai-institute

uam-analyst

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

uam_engine.py

Core simulation engine — DataLoader, eVTOL, Vertiport, UAMSim, dispatch policies, KPI collection. Pure logic, no GUI.

uam_web_app.py

Streamlit dashboard for running simulations and visualizing results (maps, charts).

uam_mcp_analyst.py

MCP server exposing the simulator as tools for Claude Desktop ("Strategic Operations Analyst" — run simulations, analyze KPIs, what-if studies).

uam_worker.py

Detached background process for long-running simulation/sensitivity jobs, spawned by the MCP server.

uam_lausa.py

LAUSA — Latent-factor Analysis of Urban Suitability & Attractiveness, a pre-simulation site-selection module for scoring candidate vertiport locations.

uam_app.py

Single packaged entry point (dispatches to dashboard / --mcp-server / --worker modes) used when building the standalone executable.

C_HGA/

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/MinGW Makefile included)

pip install -r requirements.txt

Running

Dashboard (dev):

streamlit run uam_web_app.py

MCP server (for Claude Desktop):

python uam_mcp_analyst.py

Add 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 job

Building the standalone executable

pyinstaller UAMSimulator.spec

Output 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.

F
license - not found
-
quality - not tested
A
maintenance

Maintenance

Maintainers
Response time
Release cycle
1Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/urban-ai-institute/kra33-prismx-uam-vertiport-simulation'

If you have feedback or need assistance with the MCP directory API, please join our Discord server