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xmpuspus/ph-civic-data-mcp

by Various

The first multi-source MCP server for Philippine civic data: earthquakes, weather, typhoons, procurement, population, poverty

X

MCP

xmpuspus/ph-civic-data-mcp

Added 1 June 2026

#civic-tech #claude #mcp #model-context-protocol #open-data #pagasa #philgeps #philippines

Overview

An open-source MCP server that aggregates multiple Philippine civic data sources into a single interface. It provides access to datasets on earthquakes, weather, typhoons, procurement, population, and poverty via the Model Context Protocol. Written in Python, it aims to simplify querying diverse government and public data for developers.

Best for

Best for
Developers building civic or data-driven applications focused on the Philippines

Use cases

  • Query recent earthquake events and magnitudes
  • Retrieve current weather and typhoon warnings
  • Access procurement records and population statistics

Notes

An open-source MCP server that aggregates multiple Philippine civic data sources into a single interface. It provides access to datasets on earthquakes, weather, typhoons, procurement, population, and poverty via the Model Context Protocol. Written in Python, it aims to simplify querying diverse government and public data for developers.

2 stars on GitHub. Last updated 2026-05-18. Licensed MIT.

Use cases

  • Query recent earthquake events and magnitudes
  • Retrieve current weather and typhoon warnings
  • Access procurement records and population statistics

Pros

  • Unifies multiple civic datasets under one protocol
  • Open source and Python-based, easy to extend
  • Covers a broad range of Philippine public data

Cons

  • Low star count suggests limited community validation
  • Data freshness and reliability depend on upstream sources
  • Documentation and examples may be sparse

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

Pros

  • Unifies multiple civic datasets under one protocol
  • Open source and Python-based, easy to extend
  • Covers a broad range of Philippine public data

Cons

  • Low star count suggests limited community validation
  • Data freshness and reliability depend on upstream sources
  • Documentation and examples may be sparse