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ovf-data-mcp

by bezilaszlo
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Python

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3/5.0

    Scored across 10 tools

    Disambiguation4/5

    Each tool targets a distinct purpose: dataset discovery, schema inspection, aggregation, soil-depth comparison, drought declarations, measurement-type metadata, station lookup, proximity search, raw observations, and coverage inspection. The only mild overlap is find_stations vs nearest_stations, but the descriptions (registry/name search vs geographic proximity) make the distinction fairly clear.

    Naming Consistency2/5

    The naming is inconsistent, mixing verb-based names (discover_datasets, describe_dataset, aggregate_observations, compare_soil_depths, list_measurement_types, find_stations, get_observations, inspect_coverage) with noun-phrase names (water_shortage_districts, nearest_stations). Some use verb_noun (find_stations, describe_dataset), others drop the leading verb (nearest_stations, water_shortage_districts). The 'describe_dataset' vs 'discover_datasets' singular/plural mismatch also adds inconsistency.

    Tool Count5/5

    Ten tools is a well-scoped surface for an observational water data domain, covering discovery, metadata, querying, and analysis. Each tool serves a distinct function and none feels like filler; the count is squarely in the ideal 3-15 range.

    Completeness4/5

    The surface covers discovery, metadata inspection, raw observation retrieval, aggregation, soil-depth comparison, station lookup, proximity search, and drought declarations. Minor gaps include no direct update/delete (not applicable to a read-oriented data service) and possibly no explicit download/export tool, but the core observational querying workflow appears complete.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

    Resources