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cdmx-mcp

by devcsar
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Python
Remote

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    TDQS

    B3.4/5.0

    Scored across 9 tools

    Disambiguation4/5

    Most tools have distinct purposes targeting different CDMX data domains (crime, air quality, business, bikes, datasets), but some overlap exists between 'aggregate' and 'query_records' as both query datasets with SQL-like capabilities. The descriptions help differentiate them, with 'aggregate' focused on server-side GROUP BY operations and 'query_records' on general record retrieval.

    Naming Consistency3/5

    Naming conventions are mixed but generally readable. Most tools use snake_case (e.g., 'air_quality_now', 'crime_hotspots'), but there are deviations like 'denue_near' (abbreviation) and 'ecobici_status' (brand name). Verb styles vary from descriptive nouns ('cache_stats') to action-oriented phrases ('list_datasets'), lacking a uniform pattern.

    Tool Count5/5

    With 9 tools, the count is well-scoped for a city data server covering multiple domains (crime, environment, transportation, business). Each tool serves a clear purpose, such as querying datasets, retrieving specific data types, or listing metadata, making the set comprehensive without being overwhelming.

    Completeness4/5

    The toolset provides strong coverage for accessing and querying CDMX open data, including listing, describing, and querying datasets, along with specialized tools for crime, air quality, bikes, and businesses. Minor gaps exist, such as no explicit update or delete operations (reasonable for read-only public data) and limited filtering options in some tools, but core workflows are well-supported.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources