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Alternatives to carbon-geo-mcp

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    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to inspect geospatial vector and raster metadata and run bounded analysis on administrator-registered datasets, including planar buffering, clipping, spatial joins, and PNG map rendering delivered as checksummed artifacts. All data access is read-only and controlled through a curated dataset catalog.
      Apache 2.0
    • A
      license
      A
      quality
      C
      maintenance
      Gives 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.
      6
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables 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
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables 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
    • F
      license
      A
      quality
      B
      maintenance
      Enables 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

    A3.9/5.0

    Scored across 7 tools

    Disambiguation4/5

    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.

    Naming Consistency4/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivityMaintained
    ResponsivenessSyncing