Govparse Government Data Gateway

govcon_feed_subawards

Which companies just received a federal subaward — and which are getting their FIRST one? Bulk feed over USAspending subawards (the File F prime→sub cascade), newest-first by action date: sub-recipient, prime, agency, amount (banded), NAICS, and date. new_only isolates first-time subawardees (no prime award on record) — the new-to-govcon growth signal. Flat rows, cursor-paginated up to 1000/page, filterable by state, NAICS, agency, minimum amount, or since. Observational public records as filed. [price: $0.05/row]

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Rows per page (default 500, cap 1000).
`naics`	No	NAICS code prefix(es), CSV.
`since`	No	Only subawards with an action date on or after this date (YYYY-MM-DD).
`state`	No	Place-of-performance state code(s), CSV.
`agency`	No	Awarding agency or sub-agency name fragment.
`cursor`	No	Opaque page cursor — pass the previous page's next_cursor unchanged.
`new_only`	No	true = only first-time subawardees (sub-recipients with NO prime award on record).
`min_amount`	No	Minimum subaward amount in USD.

Schema Changelog

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

AddedJul 28, 2026

TDQS

A4.3/5.0

Behavior4/5

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

No annotations provided, so the description fully bears the burden. It discloses the observational nature ('as filed'), pricing ($0.05/row), pagination (cursor, up to 1000/page), and filtering options. It does not explicitly state read-only or auth requirements, but the pricing and public record mention strongly imply it's a read operation.

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?

The description is a single paragraph but front-loads the core value proposition ('Which companies just received...') before detailing attributes. It is information-dense without being overly verbose, though it could be slightly more structured for skimmability.

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?

Without an output schema, the description lists the key fields returned (sub-recipient, prime, agency, amount banded, NAICS, date) and explains pagination and filtering. It provides sufficient context for an AI agent to understand the tool's behavior and output shape, though exact field names and data types are missing.

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?

All 8 parameters are described in the input schema (100% coverage), so baseline is 3. The description adds conceptual value by explaining new_only as 'first-time subawardees' and the meaning of cursor pagination, enhancing understanding beyond the schema alone.

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 provides a bulk feed of federal subawards from USAspending, identifying both current and first-time sub-recipients. It distinctively describes the scope and fields returned, differentiating from sibling tools like govcon_feed_awards (prime awards) and govcon_subaward_search.

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 implies usage for bulk subaward discovery and filtering, including new_only for first-time subawardees. While it does not explicitly contrast with sibling tools, the context of 'feed' versus 'search' and the mention of cursor pagination give clear signals for expected use cases.

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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