Govparse Government Data Gateway

partd_feed_prescribing

Which prescribers just entered a drug class this Medicare Part D year — e.g. new GLP-1 prescribers in PY2024? Bulk feed over CMS Part D Prescribers by Provider and Drug, aggregated per NPI to a drug class and differenced PY2023 → PY2024: direction=new_entrant returns new-to-class prescribers, de_adopter those whose class volume fell. Flat rows, cursor-paginated up to 1000/page, by drug_class, specialty, and state. Observational public Part D records — never a clinical recommendation. [price: $0.05/row]

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Rows per page (default 500, cap 1000).
`state`	No	Prescriber state code (two letters).
`cursor`	No	Opaque page cursor — pass the previous page's next_cursor unchanged.
`direction`	No	new_entrant (default; wrote the class in PY2024 with ≤ max_prior_claims in PY2023 — the new-to-class buying moment) \| de_adopter (class volume fell ≥ min_pct_drop%).
`specialty`	No	Prescriber specialty (Prscrbr_Type), e.g. Endocrinology.
`drug_class`	No	Therapeutic drug class: GLP-1 \| SGLT2 \| DPP-4 \| Statin. Default GLP-1.
`min_pct_drop`	No	de_adopter only: minimum percent decline in class volume PY2023 → PY2024.
`min_claims_2024`	No	Minimum class claims in PY2024.
`max_prior_claims`	No	new_entrant only: maximum PY2023 class claims allowed (0 = truly new to the class).

Schema Changelog

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

AddedJul 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses pricing ($0.05/row), observational nature (never clinical), pagination (cursor-based, up to 1000/page), and the two behavioral modes (new_entrant, de_adopter). Missing rate limits or authentication details, but overall transparent.

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

Conciseness4/5

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

Description is 5-6 sentences, front-loaded with purpose, and covers key aspects without redundancy. It is well-structured but could be slightly more compact.

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 9 parameters, no output schema, and moderate complexity, the description covers aggregation logic, directions, pagination, filters, and a disclaimer. It lacks output field descriptions and rate limits, but is otherwise complete.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The tool description adds minimal extra meaning beyond the schema, mainly repeating examples already in parameter descriptions. No additional context beyond what schema provides.

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 clearly states the tool's purpose: to find prescribers who are new entrants or de-adopters of a drug class in Medicare Part D, with a specific example. It distinguishes from siblings by its unique focus on Part D prescribing data.

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 provides explicit use cases (new entrants, de-adopters) and explains filtering by drug class, specialty, state. It does not explicitly mention when not to use or alternatives, but the context is sufficiently clear for common scenarios.

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

All Govparse Government Data Gateway tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?