Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
jiskta-mcp
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-mcpAvailable tools
| Tool | Description |
|---|---|
query_climate | Historical air quality + ERA5 met data for a bounding box |
query_climate_point | Same, for a single lat/lon point |
estimate_query_cost | Dry-run — check credit cost before querying |
geocode | Address → coordinates (113M housenumbers, global) |
reverse_geocode | Coordinates → nearest address |
enrich_location | Coordinate → NUTS3 region + WRI water risk + nearest facility |
water_risk | WRI Aqueduct 4.0 water risk for a bounding box |
find_facilities | Nearest E-PRTR industrial facilities within a radius |
get_coverage | Check available data months before querying |
spatial_link | Aggregate raster data to NUTS3 regions or countries |
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