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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

A4.6/5.0
Behavior5/5

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

The description goes well beyond the read-only/idempotent annotations by disclosing source fan-out behavior, the USPTO PatentsView API sunset with soft-fail, the expected empty fda_products for small-molecule-only companies, and the meaning of sources_used/sources_failed. It also explains how private companies resolve. There is no contradiction with 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 densely packed and front-loaded with query examples and the 'always prefer' directive before enumerating output sections. Some example phrases are near-duplicates, so it is slightly less concise than ideal, but overall the structure is logical and every substantive section earns its place.

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 fully documents the return shape: cik, resolved fields, recent_filings limits and URIs, fundamentals fields, patents, federal contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also explains failure and empty-result semantics. Nothing needed to invoke the tool correctly is missing.

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%: both `type` and `value` are clearly documented in the schema. The description restates the accepted value shapes (ticker, CIK, company name) and the interchangeability of `type`, but adds little new semantics beyond the schema. Baseline 3 is appropriate.

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 invocation examples and clearly states the tool's function: 'full cross-source profile of a US public company in ONE parallel call.' It names the resource (US public company), the action (profile/research), and explicitly contrasts itself with 'single-pack SEC/XBRL/news lookups' so an agent can differentiate it from related 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 directs usage: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also describes what happens for private companies (resolved:false with a notes line) and notes that `type` accepts 'company' or 'ticker' interchangeably. This gives clear when-to-use, when-to-avoid, and alternative-condition guidance.

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

A3.6/5.0
Disambiguation4/5

The 11 fb_* Facebook tools are clearly separated by resource (account vs campaign vs adset) and action (list vs get vs create), and the Pipeworx research tools each have distinct roles (router, grounded, profile, compare, research). However, ask_pipeworx, ask_pipeworx_beta, and deep_research overlap in routing/fan-out behavior, and ai_visibility_check vs scan_competitor_ai_presence are near-identical in purpose, creating some ambiguity.

Naming Consistency3/5

The 11 fb_* tools follow a consistent fb_verb_noun pattern (except fb_get_campaign vs fb_list_*), but the remaining 25+ tools mix verb-first (ask_pipeworx, compare_entities, resolve_entity), noun-first (entity_profile, recent_changes, polymarket_edges), and generic names (forget, recall, remember). Pipeworx tools use verb_noun mostly consistently (ask_pipeworx, discover_tools, resolve_entity) but the overall set blends two naming cultures without a unifying prefix or pattern.

Tool Count3/5

36 tools is on the heavy side for one server. The Facebook ads domain only needs ~11 tools, while the rest are a sprawling Pipeworx research/meta platform (memory, subscription, prediction-market, web-tooling, AI-visibility) that feels like several servers merged into one. Each area is internally coherent, but as a single MCP server the count is bloated.

Completeness4/5

The Facebook ads surface covers list accounts/campaigns/adsets and read campaigns/insights, but notably lacks create/update/delete operations for campaigns and adsets, so the ad-management workflow has dead ends. The Pipeworx research side is extremely complete for data lookup (router, grounded, deep research, entity profiles, comparisons, verification), though the memory/subscription tools introduce a separate domain that is only thinly supported.

Resources