Jiskta

PyPI License: MIT

MCP server for the Jiskta Climate & Environmental Data API. Gives AI assistants direct access to historical air quality, ERA5 meteorology, water risk, geocoding, and industrial facility data — without writing any code.

What is MCP?

Model Context Protocol is an open standard that lets AI tools (Claude Desktop, Cursor, Cline, Copilot Workspace) call external APIs in conversation. This package runs on your machine, not on any server — it just proxies calls to the Jiskta API using your API key.

Quickstart

Get an API key: jiskta.com/dashboard — free credits included on signup.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "jiskta": {
      "command": "uvx",
      "args": ["jiskta-mcp"],
      "env": {
        "JISKTA_API_KEY": "sk_live_your_key_here"
      }
    }
  }
}

No Python installation needed — uvx downloads and runs the package automatically.

Cursor / Cline / other MCP clients

Use the same config format your tool supports. The command is always uvx jiskta-mcp with JISKTA_API_KEY set.

Manual install

pip install jiskta-mcp
export JISKTA_API_KEY=sk_live_...
jiskta-mcp

Available tools

Example conversations

Once connected, you can ask things like:

"What was the average NO₂ level in Brussels in 2023?"

"Is there any industrial pollution risk near Industrieweg 1, Antwerp?"

"Compare PM2.5 trends across NUTS3 regions in the Ruhr area from 2018 to 2023."

"What's the water stress level for our distribution centre at 51.5°N, 4.3°E?"

"How many days did PM10 exceed the EU limit in Paris last year?"

Data sources

• Air quality: Copernicus CAMS EU reanalysis (0.1°, 2013–present) and CAMS Global (0.75°, 2020–present)
• Meteorology: ECMWF ERA5 reanalysis (0.25°, 2013–present)
• Water risk: WRI Aqueduct 4.0 (2023), global
• Industrial facilities: EEA E-PRTR, ~97,000 EU facilities (CC BY 4.0)
• Geocoding: OpenStreetMap (CC BY-SA)

Pricing

Credits are consumed per tile scanned (geographic area × time period × variable). Use estimate_query_cost before large queries. Top up at jiskta.com/dashboard.

Links

• API documentation: jiskta.com/docs
• Python SDK: github.com/jiskta/jiskta-python
• Examples: github.com/jiskta/jiskta-examples
• Issues: github.com/jiskta/jiskta-mcp/issues