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Glama

Phoenix Number

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

A4.8/5.0
Behavior5/5

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

Annotations already mark it read-only, idempotent, and non-destructive; the description adds significant behavioral context: USPTO PatentsView sunset soft-fail, GDELT→GNews fallback, empty sections meaning real 'no data', sources_used/sources_failed status, and resolved_from/resolved_to behavior. No contradiction with 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 front-loads intent with user phrasings and the 'ALWAYS PREFER' guidance, and every sentence carries useful information. However, the middle is an extremely dense block of output-field details and source lists, which could be structured or broken up for easier consumption.

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, the description must explain return values, and it does so thoroughly: every result section, fallback, edge case, and status field is documented. It also covers failure semantics for private companies and sunset APIs, so the agent can set expectations 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?

Input schema already covers both parameters at 100%, but the description enriches them with concrete examples ("AAPL", "0000320193", "Moderna"), states type and value are interchangeable, and clarifies name resolution via SEC EDGAR. This goes beyond the schema without being redundant or incomplete.

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 clearly states the tool builds a 'full cross-source profile of a US public company in ONE parallel call', with concrete example user phrasings. It distinguishes itself from chaining single-pack SEC/XBRL/news lookups, making the tool's role unmistakable even among siblings.

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 over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view'. It also sets scope boundaries: US public companies, accepts ticker/CIK/name, and notes that private companies return resolved:false rather than failing.

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

A3.5/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying sources; bet_research, polymarket_edges, and polymarket_arbitrage all find betting opportunities. entity_profile, recent_changes, and compare_entities similarly overlap on company data. The set includes near-duplicates, making misselection likely.

Naming Consistency3/5

All names use snake_case and lowercase, but the verb-noun pattern is inconsistent: some are verb-first (discover_tools, validate_claim), others noun-first (entity_profile, bet_research, pipeworx_trending), and some are noun-noun (polymarket_arbitrage). Prefixes like ask_pipeworx and polymarket_ provide some consistency, but overall naming style is mixed yet readable.

Tool Count2/5

With 32 tools, this is above the comfortable range for a coherent server. The count is bloated by three near-identical ask_pipeworx variants, six polymarket tools, and several overlapping meta-tools. A smaller, more focused set would be more appropriate.

Completeness3/5

As a data-access platform, it covers lookup, grounded answers, deep research, entity comparison, validation, and discovery, plus memory and subscription lifecycle. However, the set is a grab-bag with no unified purpose; there are dead ends like no way to directly call the 5,354 underlying tools except through ask_pipeworx, and the joke tool adds nothing to any workflow. The heterogeneous scope makes it hard to assess true completeness.

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