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Glama

genderize

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 description goes far beyond the readOnly/openWorld/idempotent annotations by disclosing parallel fan-out behavior, soft-failure on USPTO PatentsView sunset, expected empty fda_products for companies with only small-molecule drugs, sources_used/sources_failed semantics, private-company resolved:false behavior, and interchangeable type/value handling. This richly compensates for the absence of an output schema.

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 long, but the tool is complex and has no output schema, so the detailed return-field breakdown earns its place. It is front-loaded with intent examples and structured by output section; minor redundancy with the schema's type/value descriptions keeps it from 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, read-only research tool with two parameters and no output schema, the description covers inputs, per-source return sections, failure semantics, edge cases, and even expected 'no data' behavior. It is essentially self-sufficient for an agent to invoke 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%, but the description adds meaningful semantics: 'names resolve via SEC EDGAR's company-name match,' both type values behave identically, and what happens for private companies (resolved:false with a notes line). It complements the schema with examples and edge-case behavior rather than merely restating it.

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 ('Tell me about X', 'research Acme', 'brief me on Tesla') and then defines the tool as 'full cross-source profile of a US public company in ONE parallel call.' This states a specific task and output, and distinguishes it from 'chaining single-pack SEC/XBRL/news lookups,' so an agent can identify when this tool is the right one.

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 gives clear context: use when the user wants a holistic view, and explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups.' It does not name specific sibling tools or state exclusions, but the guidance is sufficient to route most holistic research requests here.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All genderize toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools route the same style of query to the same Pipeworx catalog: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in purpose, and ask_pipeworx_beta is explicitly identical to ask_pipeworx. The prediction-market tools also blur together, with bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all covering overlapping analysis territory.

Naming Consistency3/5

The tools are consistently lowercase snake_case, but the naming convention is mixed: some are verb_noun (predict_gender, generate_llms_txt), some are noun phrases (entity_profile, recent_alerts), some are bare verbs (remember, forget), and many share domain prefixes like ask_pipeworx or polymarket_. It is readable, but there is no single predictable pattern across the set.

Tool Count2/5

At 33 tools, this exceeds the 25+ threshold where the surface becomes hard to navigate. More importantly, the count does not match the server's apparent genderize identity: the vast majority of tools are unrelated Pipeworx research, prediction-market, memory, and subscription utilities bolted onto a two-tool gender-prediction core.

Completeness3/5

As a broad research assistant, the set is substantial: it covers question routing, grounded verification, deep research, entity profiles, comparisons, change feeds, memory, and subscriptions. However, the actual genderize domain is thin—just two prediction tools with no batch, supported-country, or accuracy endpoints—and several unrelated capabilities feel bolted on, making coverage uneven.

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