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Hk Elegislation

by io.github.pipeworx-io
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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 (the latest ANNUAL Revenues, NetIncomeLoss, Cash, diluted EPS, capex and operating cash flow, each with its period_end, SEC form and the accession of the filing it came from — plus EntityCommonStockSharesOutstanding, which is the newest COVER-PAGE share count the filer has published rather than an annual one, so its period_end is a cover date after the quarter closed and it is the right per-share denominator; a filer tagging a fallback concept appears under the name of that tag instead, e.g. PaymentsToAcquireProductiveAssets instead of PaymentsToAcquirePropertyPlantAndEquipment); 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. sources_skipped is the third state: a leg we deliberately did NOT run, each entry carrying a reason token and a plain-English detail (the Purple Book is skipped for a filer SEC classifies outside the life-science SIC bands, since it lists only 351(a)/(k) biologics licence holders). 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"). A NAME now resolves through the national business registers as well as SEC EDGAR, so a non-US company returns its register identity and record rather than `resolved: false`.
countryNoOPTIONAL ISO 3166-1 alpha-2 of the business register you mean ("NO", "FI", "GB", "JP", "FR", "EE", "BR"). Only affects a NAME lookup, and is often what separates the company you mean from a US namesake. When a name resolves to a register entity with no CIK, the SEC/CIK-keyed sections are reported under `sources_skipped` with reason `not_queried` — they were never asked, which is not the same as having failed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnly/idempotent/openWorld/non-destructive), and the description adds substantial context beyond them: the three-state sources_used/sources_failed/sources_skipped model, the meaning of an empty section, the PatentsView soft-fail, and the deliberate Purple-Book skip. This is exactly the operational detail an agent needs.

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

Conciseness2/5

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

The useful use-case examples are front-loaded, but the body is a single enormous sentence with deeply nested parentheticals (the diluted-EPS cover-page aside, the fallback-tag aside, the Purple-Book aside). The density of genuinely relevant detail does not excuse the wall-of-text structure that makes it hard to scan.

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?

With no output schema and a highly complex multi-source fan-out, the description carries the burden well: it enumerates every returned section, the per-source state tokens, and the name-resolution behaviour. An agent knows what to expect back and how to interpret partial results.

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 coverage is 100%, so the schema already documents type/value/country, and the description largely restates that (type interchangeability, accepted value shapes). It adds some real meaning — country 'only affects a NAME lookup' and the resolved:false path for private companies — but not enough to exceed the baseline when the schema does the heavy lifting.

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 states a specific verb and resource — a 'full cross-source profile of a US public company in ONE parallel call' — and enumerates the exact data legs (SEC EDGAR, XBRL, USPTO, USAspending, etc.). It clearly distinguishes itself from siblings like company_facts, deep_research and resolve_entity by covering the holistic-view use case.

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?

It gives an explicit selection rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' with concrete trigger phrasing ("Tell me about X", "research Acme"). The alternative and the condition that selects it are both stated.

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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