sih-br-mcp
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TDQS
Scored across 12 tools
The CSAP-vs-general split is mostly clear (get_icsap vs get_hospitalizations, get_icsap_indicators vs get_hospitalization_rates), but the analysis family overlaps heavily: compare_icsap_trends, compare_regions and get_hospitalization_trends all produce cross-UF/time comparisons, and rank_csap_groups largely duplicates a grouped get_icsap call. Descriptions are unusually detailed and do help, but an agent must read them carefully to pick the right comparison tool.
All 12 tools follow a consistent snake_case verb_noun pattern (get_, list_, compare_, rank_, classify_) with no case or style mixing. Names are predictable and readable, with the only variation being domain terminology (csap/icsap/hospitalization) rather than naming convention.
12 tools sits comfortably in the well-scoped range, covering query, aggregation, trends, rates, indicators, ranking, comparison, classification and metadata without obvious redundancy padding. Each tool corresponds to a recognizable analytical need for this dataset.
The surface covers the full analytical lifecycle for SIH-SUS/CSAP data: raw queries, aggregation, rates, indicators, trends, rankings, comparisons, CSAP classification and year/chapter/group metadata. Minor gaps remain — no geography lookup (UF/municipality codes and names) and no general CID-10 code description lookup beyond chapters, which agents will need for filter construction.