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Glama

Ooni

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

A4.4/5.0
Behavior5/5

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

Annotations already cover safety (readOnly/idempotent/non-destructive), so the description's rich behavioral detail — GDELT→GNews fallback chain, USPTO PatentsView May 2025 sunset soft-fail, fda_products expected-empty for small-molecule-only companies, sources_used/sources_failed semantics, and 'empty section is a real no-data, not a bug' — adds substantial value beyond the annotations. No contradiction exists.

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?

Every sentence earns its place — triggers, purpose, source fan-out, return fields, input formats, failure semantics — and the most decision-relevant content (purpose and routing) is front-loaded. However, it is a dense single block of text with no paragraph breaks, which reduces scanability for an agent skimming for key facts.

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 carry the return-format burden, and it does — enumerating every result section, source-failure reporting, input normalization, and the private-company resolved:false path. For a complex multi-source tool this is unusually complete; only minor aspects like expected latency are left implicit (already covered by the 'ONE parallel call' claim).

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%, so the schema already documents both parameters, including that type accepts 'company'/'ticker' interchangeably and value accepts ticker/CIK/company name. The description adds marginal value by noting resolved:false behavior for private companies and SEC EDGAR name resolution, but the schema carries the semantic burden.

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?

Opens with concrete trigger phrases ('Tell me about X', 'research Acme', 'brief me on Tesla') then states the core function precisely: 'full cross-source profile of a US public company in ONE parallel call.' It enumerates the exact return sections (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI), which distinguishes it sharply from siblings like resolve_entity (pure resolution) and compare_entities (comparison).

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?

Provides explicit routing guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' plus trigger phrases and the full set of accepted input shapes. It does not explicitly say when to prefer siblings instead (e.g., compare_entities for comparing two companies, resolve_entity for a bare lookup), which would have closed the loop.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but the ask_pipeworx family (including beta and grounded) and deep_research overlap in functionality, causing some ambiguity. The OONI tools are well-differentiated, and other tools are distinct.

Naming Consistency4/5

Naming predominantly follows a verb_noun pattern with underscores, but some tools like generate_llms_txt and pipeworx_feedback deviate slightly. Overall consistent with minor inconsistencies.

Tool Count4/5

35 tools is on the higher side but justified by the broad domain coverage (data lookup, censorship monitoring, prediction markets, memory, etc.). Each tool serves a specific purpose, making the count reasonable.

Completeness4/5

The tool surface covers core workflows comprehensively, including data retrieval, censorship analysis, prediction market evaluation, and memory management. Minor gaps exist (e.g., no subscription modification tool), but overall it's well-scoped.

Resources