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Aryan-Jhaveri

Statistics Canada MCP Server

by Aryan-Jhaveri
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Python
Hybrid

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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

    A3.6/5.0

    Scored across 25 tools

    Disambiguation2/5

    Multiple tools overlap in purpose, e.g., get_all_cubes_list vs get_all_cubes_list_lite, get_sdmx_data vs get_sdmx_rows, get_series_info vs get_series_info_from_vector, and two changed-series-data tools that differ only by identifier type. The detailed descriptions help, but the boundaries between these tools are subtle enough that an agent could easily pick the wrong one.

    Naming Consistency3/5

    Most tools follow a snake_case verb_noun pattern (get_, list_, create_, insert_, etc.), but the SDMX family is inconsistent: get_sdmx_data, get_sdmx_rows, get_sdmx_vector_data, and get_sdmx_structure all sound like data retrieval tools, and get_series_info vs get_series_info_from_vector is confusingly similar. Naming is readable but not perfectly predictable.

    Tool Count3/5

    At 25 tools, the server sits at the high end of 'feels heavy'. The scope is broad (Statistics Canada API plus SQLite persistence), which justifies many tools, but some functions could be merged (e.g., get_sdmx_data and get_sdmx_rows) to reduce redundancy and cognitive load.

    Completeness4/5

    The tool surface covers the full data lifecycle: cube discovery, search, metadata, series info, SDMX data, vector access, changed-data tracking, code-set definitions, plus a full SQLite persistence layer for storing and querying results. Minor gaps exist (e.g., no direct batch-metadata fetch, and lastNObservations can't combine with date ranges), but no critical dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessSlow

    Resources