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StatsMapped: Irish statistics (CSO, county & council data)

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Alternatives to StatsMapped: Irish statistics (CSO, county & council data)

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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to resolve places and statistical variables, then retrieve harmonised public statistics such as population, income, health, employment, and emissions with publisher provenance and child-place comparisons.
      437 npm
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Enables AI assistants such as Claude, ChatGPT, and GitHub Copilot to look up Irish public data from sources including CSO, Oireachtas, GeoHive, Met Éireann, NTA, Property Price Register, data.gov.ie, Smart Dublin, and the Irish Statute Book, with source, licence, and retrieval time attached.
      7
      2
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI assistants to search, preview, and query Ireland's public-sector open data catalogue, including CSV, Excel, JSON, GeoJSON, and JSON-stat resources. It supports filtering, grouping, counting, summing, and sorting datasets conversationally without manual downloads or cleanup.
      MIT
    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables LLM agents to query the Explore Local Statistics API for local area data like employment rates and demographics.
      -
    • A
      license
      A
      quality
      C
      maintenance
      Enables AI agents to perform due diligence on Irish properties by querying public datasets for planning applications, sold prices, flood risk, radon risk, and zoning.
      1
      74 npm
      8
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to search, inspect, and retrieve Irish Central Statistics Office PxStat datasets through MCP tools. Supports catalog search, metadata discovery, full-table reads, and filtered JSON-stat queries.
      450 npm
      MIT

    TDQS

    A4.1/5.0

    Scored across 6 tools

    Disambiguation4/5

    The six tools cover mostly distinct intents (metadata, area lookup, data retrieval, charts, notable changes, comparisons), and descriptions repeatedly cross-reference each other to steer selection. However, `query_data` and `compare` each pack four modes under one name, creating boundary friction (e.g. ranking latest figures in `compare` vs returning latest figures in `query_data`), and `notable_changes` overlaps with fetching recent history.

    Naming Consistency3/5

    Names mix verb_noun forms (`explain_metric`, `list_areas`, `query_data`, `get_scene`) with a bare verb (`compare`) and a noun phrase (`notable_changes`). The convention is readable and partly predictable, but not uniform across the set.

    Tool Count4/5

    Six tools is a sensible, well-scoped count for this domain. Each tool earns its place, though several are heavy multi-mode dispatchers (`query_data` and `compare` each have four modes), making the effective surface larger than six.

    Completeness4/5

    The surface covers dataset discovery, metric definitions/caveats, area listings, area-level figures with history, rankings/comparability, recent notable changes, and a chart scene. Minor gaps exist (no general name-based search, and council planning is Ireland-only), but core statistical browsing workflows are covered.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues