carbon-geo-mcp
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- AlicenseAqualityCmaintenanceGives AI assistants read-only, key-free access to Sentinel-2 NDVI time series, ESA WorldCover land-cover shares, and Copernicus 30 m elevation and slope for a point or small area, returning the scene IDs, units, and caveats behind every number. Built-in sanity checks and a verify tool recompute key values through an independent path so the assistant can flag cloudy, low-confidence, or mismatched results instead of guessing.6MIT
- AlicenseNot gradedqualityCmaintenanceEnables an AI assistant to run pre-ingest QA on utility power-grid GIS exports: score readiness, list issues with coordinates, reconcile registers against survey/LiDAR detections, map codes to library types, and draft a data request. It exposes the same checks, reconciliation and reporting operations as callable tools so a deployment engineer can work conversationally in any MCP client.MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to perform geospatial analysis, spatial data processing, vector and raster operations, coordinate transformations, spatial statistics, mapping, and access to climate, satellite, and other geospatial data through MCP-compatible clients.MIT
- FlicenseAqualityBmaintenanceEnables AI agents to inspect and repair GIS parcel datasets and to query a redacted DuckDB results store, including table listing, guarded SQL queries, governed metrics, issue lookups, case explanations and run comparisons. Repairs are always written to a new copy and every tool call is logged.11-
TDQS
Scored across 7 tools
Each tool has a clearly separate analytical purpose (inspection, area measurement, declared-area comparison, cross-layer overlap, plot QA, raster forest loss, attribute completeness). The only real overlap is between layer_info and data_completeness, since layer_info already reports per-field null counts while data_completeness goes deeper on blanks/distinct values; the descriptions make the distinction (structural describe vs. attribute QA) reasonably clear.
All names are snake_case and read predictably. The stem style is mixed—verb-led (compare_declared_area, check_overlap) versus noun/domain-led (layer_info, plots_in_strata, forest_loss_in_area, data_completeness)—but each name accurately telegraphs its operation.
Seven tools is well-scoped for a geo-validation server; each one covers a distinct step in carbon-project spatial verification (inspect, measure, compare, cross-check, plot QA, forest loss, attribute QA). No tool appears redundant or filler.
The surface covers the core QA lifecycle for project-area data: layer health, equal-area measurement, declared-vs-actual reconciliation, overlap detection, plot placement checks, forest-loss quantification and attribute completeness. Minor gaps exist—no reprojection/export or generic raster statistics beyond lossyear, and no written report output—but agents can work around these.