Govcon Intel

Entity Profile

entity_profile

Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`type`	Yes	"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
`value`	Yes	Ticker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

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

Changed3 schema fields changedSep 3, 2026

changedInput schema / properties / type / description

Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."

changedInput schema / properties / type / enum

Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]

changedInput schema / properties / value / description

Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."

AddedMay 12, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Even with readOnlyHint/openWorldHint/idempotentHint annotations, the description adds substantial behavioral context: patents source 'soft-fails until reactivated,' fda_products showing none is 'expected, not a failure,' empty sections are 'a real no data, not a bug,' private companies return resolved:false with a notes line, and news uses a GDELT→GNews fallback. This goes well beyond what annotations declare.

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

Conciseness3/5

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

The description is front-loaded and information-dense, but it is one long uninterrupted paragraph and opens with seven near-identical user-phrase examples. The lengthy source-by-source output breakdown is valuable given no output schema, yet the redundancy and lack of structural formatting keep it from being tightly concise.

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

Completeness5/5

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

For a complex multi-source tool with no output schema, the description is remarkably complete: it covers input variants, each return section, source fallbacks, soft-fail behavior, expected-empty semantics, and resolved:false handling for private companies. An agent has enough context to invoke the tool correctly and interpret unusual results.

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 description coverage is 100%, so baseline is 3. The description adds useful semantic context beyond the schema, particularly that a private company input yields resolved:false with an explicit notes line and that 'company'/'ticker' are fully interchangeable input types. Much of the value/shape explanation is redundant with the schema, but the added failure semantics justify a 4.

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 opens with multiple natural-language triggers ('Tell me about X', 'research Acme', 'brief me on Tesla') and defines the tool as 'full cross-source profile of a US public company in ONE parallel call.' It lists distinct output sections (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI), making the purpose and resource unmistakable and distinguishing it from narrower single-source lookups.

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?

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and specifies the accepted input forms (ticker, CIK, company name). It does not explicitly name sibling alternatives like compare_entities or deep_research or state when NOT to use this tool, so it falls just short of full five-level alternative routing.

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

A4.1/5.0

Disambiguation3/5

Several tools have overlapping purposes (e.g., ai_visibility_check and scan_competitor_ai_presence; ask_pipeworx, ask_pipeworx_grounded, and deep_research). However, detailed descriptions and specific use cases help agents distinguish between them in most cases.

Naming Consistency5/5

All tools use snake_case naming consistently, e.g., ask_pipeworx, compare_entities, govcon_agency_landscape. The pattern is uniform across the entire set.

Tool Count4/5

With 33 tools, the set is slightly over the typical well-scoped range. While many tools serve distinct purposes, some seem redundant (e.g., memory tools, multiple research tools), making the count feel a bit heavy.

Completeness3/5

The tool surface covers a broad range of research and intelligence domains but lacks actionable tools for core government contracting tasks like submitting bids or tracking contract performance. Several obvious operations (e.g., user profile management, submission tools) are missing.

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