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Glama

Govinfo

by io.github.pipeworx-io

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
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (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.

  1. Changed3 schema fields changed
    • 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."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already set readOnly/openWorld/idempotent/nondestructive, and the description adds substantial behavioral context: fan-out across many sources, soft-failure of the USPTO API, resolved_from/resolved_to for name lookups, sources_used/sources_failed semantics, and 'an empty section is a real no-data, not a bug.' No contradiction with annotations.

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?

Front-loaded with intent examples and a clear 'ALWAYS PREFER' directive, then a logically structured explanation of fan-out, output sections, failure modes, and parameter forms. It is long, but most clauses carry distinctive information; minor redundancy and length prevent a 5.

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 multi-source tool with no output schema, the description enumerates every major return group, source, failure mode, and input form, and explains how to interpret empty sections, soft-failed sources, and private companies. This is sufficient for an agent to invoke and interpret results 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%, so baseline is 3; the description adds meaningful value by clarifying that 'type' is interchangeable, that 'value' can be a ticker, zero-padded CIK, or company name, that names resolve via SEC EDGAR, and that private companies yield resolved:false with notes. This goes beyond the schema, though a few formatting edge cases remain unspecified.

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 concrete user intents ('Tell me about X', 'research Acme', 'brief me on Tesla') and states it returns a 'full cross-source profile of a US public company in ONE parallel call,' using specific verbs and naming the resource. This clearly distinguishes it from single-pack lookups and sibling tools like resolve_entity or compare_entities.

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

Usage Guidelines5/5

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

Explicitly frames when to use it: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' with concrete query examples. It also specifies boundaries — US public companies, accepted input forms, and private-company behavior — giving an agent both selection and exclusion criteria.

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

A3.8/5.0
Disambiguation2/5

Many tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve lookup/discovery in similar ways, and the five polymarket_* tools plus bet_research blur together. The six genuinely GovInfo-specific tools are distinct, but an agent would struggle to pick between the numerous meta and market tools, especially when the server is supposed to be about government information.

Naming Consistency3/5

Most tools follow a readable verb_noun pattern (list_collections, search_packages, get_granule, resolve_entity), but there are deviations: domain-prefixed nouns like polymarket_arbitrage, noun-ish names like pipeworx_trending, and verb phrases like ask_pipeworx_beta or generate_llms_txt. Overall it is mixed yet still navigable, not chaotic.

Tool Count2/5

36 tools is excessive for a server named Govinfo: only about six tools (list_collections, search_packages, get_package, list_granules, get_granule, search_within) actually serve that domain, while the remaining ~30 are Pipeworx meta-tools, prediction-market helpers, memory utilities, and AI-visibility checks. The count is bloated relative to the apparent scope and dilutes the server's identity.

Completeness3/5

The core GovInfo workflow is present — list collections, search packages, fetch package metadata, list and fetch granules, and semantically search within fetched text. However, there is no tool that directly downloads or returns the full text/PDF/XML content of a package or granule; agents only get links, so a full-document workflow requires an external fetch. The unrelated tools do not fill that gap and instead distract from the domain.

Resources