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Glama

Wiktionary

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

A4.6/5.0
Behavior5/5

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

Annotations already mark readOnly, openWorld, idempotent, and non-destructive, and the description adds substantial context: parallel fan-out across multiple sources, soft-fail behavior for sunset APIs, expected-empty FDA sections, sources_used/sources_failed semantics, and resolved:false for private companies. It does not contradict 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 long but appropriately dense for a multi-source tool, and it front-loads trigger phrases and the ALWAYS PREFER directive. However, it is a single wall-of-text paragraph and repeats some schema information, which slightly reduces scannability.

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?

Despite having no output schema, the description thoroughly documents every return section, source, failure mode, and edge case. An agent has everything needed to call this tool correctly and interpret its response, including empty-section semantics and private-company behavior.

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% and both parameters are already fully described in the schema, including the interchangeability of 'company'/'ticker' and the accepted value shapes. The description adds examples and resolved_from/resolved_to outcome details, but it does not introduce new parameter syntax or meaning beyond what the schema already provides.

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 trigger phrases and states a specific verb+resource: 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes itself from chained single-pack SEC/XBRL/news lookups, so an agent can identify when this tool is the intended holistic lookup.

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?

Usage guidance is explicit: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies what inputs are valid and what happens for private companies, leaving no ambiguity about when to invoke it.

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

B3.1/5.0
Disambiguation1/5

The toolset is overwhelmingly fragmented: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points; polymarket_edges, polymarket_arbitrage, and polymarket_fill_risk heavily overlap; and ai_visibility_check vs scan_competitor_ai_presence cover the same task. The five actual Wiktionary tools are distinct but are lost among dozens of unrelated research and prediction-market tools, making selection highly ambiguous.

Naming Consistency3/5

All names use snake_case and several logical prefixes (ask_pipeworx, polymarket_, pipeworx_) create local patterns. However, the naming mixes noun-style commands (definition, etymology, pronunciations, summary) with verb-style commands (search, remember, forget, validate_claim), and no consistent verb_noun convention carries across the whole set.

Tool Count1/5

36 tools is already heavy, but the deeper problem is that only 5 tools actually belong to a Wiktionary server while 31 tools serve unrelated Pipeworx, Polymarket, memory, and marketing-audit functions. The count is wildly inappropriate for the declared server purpose.

Completeness2/5

The Wiktionary-relevant tools cover basic word lookup—search, summary, definition, etymology, pronunciations—but omit common dictionary operations like translations, usage examples, inflected forms, or random entries. The non-Wiktionary majority does not fill these gaps; it just makes the surface area incoherent and hard to reason about.

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