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Glama

Newsdata

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?

The description goes far beyond the annotations by disclosing the fan-out behavior, sources_used/sources_failed semantics, expected empty sections, the GDELT→GNews fallback, and resolved:false behavior for private companies. It also explicitly frames 'no data' as a real result, not a bug.

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 densely informative and front-loaded with example queries and the key 'ALWAYS PREFER' guidance. The source list is necessary for a fan-out tool, though a bulleted or more structured layout would improve 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?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: CIK, company_name, resolved_from/to, recent_filings, fundamentals, patents, contracts, FDA products, hiring, news, LEI, and source status. It also covers edge cases like private companies and expected empty sections.

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?

Most parameter meaning is already in the 100%-covered schema, but the description adds useful behavioral context: private companies return resolved:false with a notes line rather than a bare failure, and the interchangeable type handling is reinforced with concrete examples.

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 is unequivocal about what the tool does: it produces a full cross-source profile of a US public company in one parallel call. It enumerates all data sources and return components, making it easy to distinguish from single-purpose lookup tools.

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 the agent to prefer this tool over chaining single-pack SEC/XBRL/news lookups when a holistic view is requested. It also clarifies accepted input forms and the private-company fallback, giving clear usage boundaries.

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

A3.8/5.0
Disambiguation3/5

Most tools have detailed usage guidance, but there are several overlapping entry points: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research; discover_tools vs suggest_questions; entity_profile vs recent_changes; and ai_visibility_check vs scan_competitor_ai_presence. The descriptions help, but the boundaries are not always crisp enough to prevent misselection.

Naming Consistency3/5

Names are mostly lower_snake_case, but conventions vary widely: some are verb-first (list_subscriptions, resolve_entity), some are noun/adjective phrases (recent_changes, entity_profile), and several use brand prefixes (pipeworx_trending, polymarket_arbitrage). The naming is readable but lacks a single predictable pattern.

Tool Count2/5

34 tools is well past the 25+ threshold and the scope sprawls beyond news/data into prediction-market arbitrage, npm dependency scanning, AI visibility audits, memory, subscriptions, and llms.txt generation. Each tool may be useful, but the set feels like several different servers merged into one, making it oversized and harder to navigate.

Completeness4/5

The research workflow is well covered: universal routing, grounded verification, deep research, entity resolution/profiles/comparisons, change feeds, discovery/onboarding, subscriptions, and memory all exist. Minor gaps include no direct pipeworx:// citation fetch tool and no broader account/profile management, but agents can work around these.

Resources