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lindas-mcp

by malkreide
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Python
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    TDQS

    A4.6/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool owns a clearly distinct step in the workflow: discover cubes, list publishers, inspect structure, read observations, resolve municipalities, check status, or run raw SPARQL. Even the structurally related tools are easy to tell apart because their responsibilities are explicitly separated.

    Naming Consistency4/5

    Six of seven tool names follow a clear verb_noun snake_case pattern like search_cubes, list_publishers, and get_cube_structure. api_status is the only outlier, being a noun phrase rather than a verb-led name, but the overall style remains predictable.

    Tool Count5/5

    Seven tools is well-scoped for a read-only statistical data cube server: discovery, structure inspection, data reading, publisher filtering, municipality resolution, status checking, and a raw SPARQL escape hatch. Each tool earns its place without redundancy.

    Completeness4/5

    The set covers the core cube workflow well: search, publisher filtering, structure inspection, observation reading, and key resolution. Minor gaps like first-class code-list entry enumeration or direct version-history browsing are not offered as dedicated tools, though run_sparql and search flags provide workarounds.

    Maintenance

    ActivityActive
    ResponsivenessUnresponsive

    Resources