Govparse Government Data Gateway

hcris_staffing_decline_search

Which hospitals cut staffing year-over-year? Compares each CCN's latest HCRIS fiscal year to the prior on a staffing ratio (metric=fte_per_bed default, or fte_per_discharge) and returns those whose ratio DROPPED. Filter by state, min_pct_drop, year, ccn, name, control_type, min_beds. Returns latest/prior ratio, delta and pct_change — an understaffing tell. CCN joins CMS timely-care. CMS public-domain records. [price: $0.05/row]

Input Schema

TableJSON Schema

Name	Required	Description
`ccn`	No	Provider CCN(s), CSV.
`name`	No	Hospital name fragment.
`year`	No	Restrict the LATEST fiscal year(s) compared, CSV.
`limit`	No	Max rows (default 25, cap 100).
`state`	No	Hospital state code(s), CSV.
`metric`	No	fte_per_bed (default) \| fte_per_discharge.
`offset`	No	Rows to skip.
`min_beds`	No	Minimum beds in the latest year.
`control_type`	No	Type of control fragment.
`min_pct_drop`	No	Minimum YoY % drop in the ratio.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedJul 28, 2026

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It explains the comparison logic (latest vs prior year), the metric options, filter parameters, and return fields (delta, pct_change). It also notes the dataset provenance (CMS public-domain) and joinability with CMS timely-care. Minor ambiguity remains about how 'latest' and 'prior' are determined across multiple fiscal years.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two focused sentences plus a price note. Every word adds value, starting with a compelling question. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 10 parameters and no output schema, the description adequately explains the comparison logic, filter capabilities, and return fields. It could clarify edge cases (e.g., missing prior year data) but overall provides sufficient context for an AI agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, providing a baseline of 3. The description adds value by explaining the default metric (fte_per_bed) and alternative (fte_per_discharge), how parameters like min_pct_drop filter results, and the output fields (latest/prior ratio, delta, pct_change). This contextualizes parameters beyond their schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('cut staffing year-over-year') and resource ('HCRIS staffing ratio'), clearly distinguishing it from siblings like 'hcris_cost_reports_search' which deals with cost reports rather than staffing declines.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states the tool is for finding hospitals with staffing declines and lists filter parameters. While it implies when to use, it does not explicitly state when not to use or compare with alternatives. However, the context of sibling tools makes the usage clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.2/5.0

Disambiguation5/5

Each tool targets a distinct domain and specific action (e.g., FDA approvals vs clearances vs recalls; firmstanding business360 dossier vs search vs screen). Even overlapping concepts like 'business360' vs 'business360_lookup' are distinguished by input (UUID vs name+state). No two tools appear to do the same thing.

Naming Consistency5/5

All tools use a consistent lowercase snake_case pattern with domain prefix (e.g., fda_*, firmstanding_*, fmcsa_*, govcon_*). Action words (search, lookup, screen, feed, stats) follow predictable usage. The naming is uniform and easy to parse.

Tool Count4/5

38 tools is on the higher end but appropriate for a comprehensive government data gateway spanning multiple agencies and datasets. Each domain has a reasonable number of tools (e.g., FMCSA: 7, OFLC: 6). Could potentially be trimmed slightly, but overall well-scoped for the stated purpose.

Completeness5/5

The tool surface covers the major government data sources comprehensively: FDA (approvals, clearances, recalls), FMCSA (carrier census, safety, insurance, etc.), FSIS, DOJ/OFLC, OSHA/EPA/DOL enforcement, SEC insider filings, clinical trials, VA facilities/opportunities/vendors, and federal contracting. No obvious gaps for the stated gateway purpose.

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