🌍 Geospatial MCP Server & AI Assistant

Author: Glenn Mossy
License: MIT

A powerful Model Context Protocol (MCP) server designed for advanced geospatial analysis, 3D terrain visualization, and autonomous drone mission planning. This project integrates real-time geospatial tools with an AI Assistant powered by open-source LLMs.

(Above: 3D Visualization of a Drone Flight Path over Terrain)

✨ Key Features

🎮 Mission Control Center

• Autonomous Mission Planning: Select from pre-defined scenarios like "Rim Survey", "Grand Canyon Tour", and "River Run".

• Safety Analysis: Automatically calculates route distance, checks for terrain collisions, and verifies safe altitude margins.

• Line of Sight (LOS): Simulates connectivity from Comms Towers and visibility from Observation Posts.

• 3D Visualization: Interactive 3D plots of the drone's flight path relative to the terrain.

🤖 AI Geospatial Assistant

• Natural Language Interface: Chat with your geospatial data using models like Qwen/Qwen2.5-72B-Instruct (via Hugging Face).

• Tool Use: The AI can autonomously call tools to answer complex questions:

  • "Can a tower at A see a rover at B?"

  • "Generate a heatmap for this area."

  • "Plan a route from X to Y."

• Visual Responses: The assistant can generate and display terrain heatmaps directly in the chat.

🛠️ Geospatial Tool Suite

• Elevation: Query high-resolution elevation data (DTED/SRTM) for any coordinate.

• Line of Sight: Calculate visibility between any two points on the globe.

• Heatmaps: Render visual elevation gradients for situational awareness.

• Distance & MGRS: Utilities for distance calculation and coordinate conversion (Lat/Lon <-> MGRS).

🚀 Getting Started

Prerequisites

• Docker (Recommended) OR Python 3.11+

• Hugging Face API Token (Free) for the AI Assistant.

Quick Start (Docker)

1. Clone the Repository:

   git clone https://github.com/gmossy/geo-mcp-server.git cd geo-mcp-server

2. Configure Environment: Create a .env file in the root directory:

   HF_TOKEN=hf_YourHuggingFaceTokenHere

3. Build and Run:

   make build make run

4. Access the Dashboard: Open http://localhost:7860 in your browser.

Local Development

Option A: Standard Pip

1. Install Dependencies:

   pip install -r requirements.txt

2. Run Server:

   ./start.sh

Option B: Using uv (Recommended for Speed)

uv is an extremely fast Python package installer and resolver.

1. Install uv (if not installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Create a Virtual Environment:

   uv venv source .venv/bin/activate

3. Install Dependencies:

   uv pip install -r requirements.txt

4. Run Server:

   ./start.sh

📂 Project Structure

• gradio_dashboard.py: The main UI application (Gradio) featuring the Dashboard, AI Assistant, and Mission Control.

• server.py: The core FastAPI server implementing the MCP protocol and geospatial logic.

• bridge.py: An adapter to connect this server to Claude Desktop or other MCP clients.

• dted/: Directory for storing elevation data tiles (DTED/SRTM).

• sample_routes/: GeoJSON files defining mission paths.

• scripts/: Helper utilities for data downloading and testing.

🔌 Connecting to Claude Desktop

You can use this server as a tool provider for Claude Desktop.

1. Ensure the server is running.

2. Configure Claude Desktop to use bridge.py:

   "geo-server": { "command": "python", "args": ["/absolute/path/to/geo-mcp-server/bridge.py"] }

📸 Screenshots

Mission Control

(Placeholder: Screenshot of the Mission Control tab showing the 3D plot and analysis report)

AI Assistant

(Placeholder: Screenshot of the AI Assistant generating a heatmap)

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Created by Glenn Mossy