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xyver/daedal-map

by Various

Open geographic query engine — ask place-based questions in natural language, get answers on a map. Covers disasters, demographics, economics, and climate data across 40+ sources.

MCP

xyver/daedal-map

Added 11 June 2026

#climate #demographics #disaster-data #earthquakes #fastapi #foss #geographic-data #geospatial

Overview

An open geographic query engine that accepts natural language questions about places and returns answers on a map. It integrates data from over 40 sources covering disasters, demographics, economics, and climate. The tool is written in Python and available on GitHub.

Best for

Best for
Developers seeking a natural language interface for geographic data exploration

Use cases

  • Query climate data for specific regions
  • Map demographic patterns across cities
  • Analyze disaster impact zones

How to use

Install

pip install -r requirements.txt

Example client config

DEPLOYMENT=local\nINSTALL_MODE=local\nRUNTIME_MODE=local\nDATA_ROOT=C:/path/to/your/local/data

Notes

An open geographic query engine that accepts natural language questions about places and returns answers on a map. It integrates data from over 40 sources covering disasters, demographics, economics, and climate. The tool is written in Python and available on GitHub.

0 stars on GitHub. Last updated 2026-06-11. Licensed MIT.

Use cases

  • Query climate data for specific regions
  • Map demographic patterns across cities
  • Analyze disaster impact zones

Pros

  • Natural language interface lowers the barrier for spatial analysis
  • Covers 40+ data sources for broad geographic queries
  • Open source and Python-based for easy customization

Cons

  • Zero stars on GitHub suggests limited community adoption
  • No evidence of active maintenance or updates
  • May require additional setup for external data source APIs

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Natural language interface lowers the barrier for spatial analysis
  • Covers 40+ data sources for broad geographic queries
  • Open source and Python-based for easy customization

Cons

  • Zero stars on GitHub suggests limited community adoption
  • No evidence of active maintenance or updates
  • May require additional setup for external data source APIs
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