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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. Added

TDQS

A4.6/5.0
Behavior5/5

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

The annotations already mark it read-only, idempotent, and non-destructive, and the description adds substantial behavioral detail beyond that: source fan-out, sources_used/sources_failed semantics, the USPTO soft-fail, FDA expectation caveat, and the private-company resolved:false behavior. There is no contradiction with the 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?

The description is dense and somewhat long, and it repeats the name-resolution and type-interchangeability details that already appear in the schema. However, it is front-loaded with examples and the key usage preference, and each added clause carries useful operational information.

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?

There is no output schema, so the description must explain return shape, and it does: cik, company_name, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also covers edge cases like empty sections, private companies, and partial source failures, making it fully actionable without further context.

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 the schema already documents that type accepts "company" or "ticker" interchangeably and that value can be a ticker, CIK, or company name. The description's examples and repeated interchangeability note are helpful but do not add meaning beyond what the schema already states.

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 phrasings and states the precise capability: a single cross-source profile of a US public company. It clearly identifies the resource and scope, and distinguishes itself from chaining individual SEC/XBRL/news lookups.

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 explicitly says to ALWAYS PREFER this tool over chaining single-pack SEC/XBRL/news lookups when the user wants a holistic view. It also covers input shaping (ticker, CIK, name) and the expected behavior for private companies, giving the agent clear routing and fallback guidance.

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

A3.7/5.0
Disambiguation2/5

The five gads_* tools are distinct, but the majority of the surface is a sprawling research/meta toolkit with many overlapping retrieval entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions, validate_claim, entity_profile, compare_entities, recent_changes, and search_within all cover overlapping information-query territory. An agent could easily misroute a question among the ask_pipeworx variants or between the general-query and company-profile tools.

Naming Consistency3/5

Domain prefixes like gads_, polymarket_, and ask_pipeworx_ provide some structure, but naming conventions are mixed: gads_list_campaigns and list_subscriptions follow verb_noun, while entity_profile, ai_visibility_check, remember, and generate_llms_txt do not. The names are readable and grouped by prefix, but they do not form one consistent pattern.

Tool Count2/5

At 36 tools this is a large surface, and the count becomes even more problematic because the server is named Google_ads while only 5 of the 36 tools relate to Google Ads. The other 31 tools are a broad Pipeworx data-research, prediction-market, memory, and subscription utility set, which makes the server feel bloated and mis-scoped for its advertised purpose.

Completeness2/5

As a Google Ads server, the surface is read-only and incomplete: it can list campaigns and ad groups, get campaign details, pull metrics, and run GAQL, but it cannot create, update, or delete campaigns, manage budgets and bids, or handle keywords, audiences, or ad creatives. The many unrelated data-research tools do not address these core Google Ads management gaps.

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