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Glama

What3words

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 declare readOnlyHint, idempotentHint, and non-destructive behavior, and the description responsibly adds behavioral context beyond that: parallel fan-out across sources, patent soft-failure after an API sunset, empty sections meaning real 'no data' rather than bugs, and resolved:false with notes for private companies. It also discloses sources_used/sources_failed semantics, which is exactly the kind of runtime behavior 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.

Conciseness4/5

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

The description is very long, but the length is justified by the absence of an output schema and the tool's many response sections. It front-loads purpose and routing guidance before enumerating fields, and each clause adds distinct information. It could be slightly better organized with headers, but no sentence is wasted.

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?

Given the tool's complexity, a simple 2-parameter schema, and no output schema, this description is exceptionally complete. It documents all major output sections, failure modes, source fallbacks, and input-resolution behavior. An agent has everything needed to select and invoke the tool 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 the description does not need to compensate for undocumented parameters. It still adds value by giving concrete examples (AAPL, 0000320193, Moderna), clarifying that type='company' and type='ticker' behave identically, and explaining how company-name resolution works through SEC EDGAR. This goes just beyond what the schema 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 high-signal example queries and immediately states the tool's function: a full cross-source profile of a US public company in one parallel call. It is clearly distinct from sibling tools like deep_research or resolve_entity by naming exact output sections and source families. The verb 'profile' plus the explicit scope makes the purpose unambiguous.

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 instructs to 'ALWAYS PREFER' this tool over chaining single-pack SEC/XBRL/news lookups when the user wants a holistic view. It also defines acceptable input shapes, notes that private companies return resolved:false, and states that 'type' accepts either value interchangeably. This gives the agent clear routing rules with concrete alternatives and conditions.

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

All What3words toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

There is significant overlap among the meta-query tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions, and validate_claim all route to the same underlying data catalog with only subtle differences in grounding or scope. Company-focused tools like entity_profile, compare_entities, recent_changes, and resolve_entity also share fuzzy boundaries. The four what3words tools are clearly distinct, but they sit awkwardly beside a much larger, partially redundant Pipeworx/prediction-market cluster.

Naming Consistency3/5

All tool names use snake_case, which provides a base level of consistency, but the naming patterns vary widely: some are verb_noun (list_languages, recall, forget), some are X_to_Y (coords_to_words, words_to_coords), some are brand-prefixed (pipeworx_*, polymarket_*), and some are bare concepts (deep_research, entity_profile, autosuggest). The ask_pipeworx family is internally consistent, as are the polymarket_* tools, but the overall set lacks a single predictable convention.

Tool Count2/5

35 tools is well above the 25-tool threshold, and the server is named What3words when only 4 of the 35 tools actually belong to that geocoding domain. Even interpreted as a general data platform, 35 tools with a heavily overlapping meta-tool layer feels bloated rather than well-scoped. The what3words-specific surface would be appropriately sized at 4-5 tools on its own.

Completeness3/5

For what3words specifically, the surface is complete: coords_to_words, words_to_coords, autosuggest, and list_languages cover the core bidirectional conversion plus discovery. However, for the broader domain the server actually serves, there are notable gaps such as no direct resolve-by-pipeworx://-URI tool and no open-web search, despite citations and external data being advertised as fetchable. The mix of geocoding, data lookup, prediction markets, memory, and subscriptions makes it unclear what complete coverage would even mean.

Resources