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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

A4.8/5.0
Behavior5/5

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

Annotations already mark the tool readOnly, openWorld, and idempotent; the description adds substantial behavior beyond that: parallel fan-out across sources, USPTO PatentsView soft-fail due to sunset, empty sections meaning real 'no data,' and private-company resolution 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 is long, but every sentence earns its place by covering a distinct source, return field, or edge case. It is front-loaded with examples and the core purpose, making the structure effective despite being verbose.

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 thoroughly: CIK/company_name, recent_filings with URIs, fundamentals, patents, federal_contracts, fda_products, hiring, news, and LEI—plus sources_used/sources_failed semantics. It also covers input variants and error behavior, leaving no critical gap for correct invocation.

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?

The input schema already provides 100% parameter descriptions, so the baseline is 3. The description adds value by reinforcing that 'company' and 'ticker' are interchangeable and explaining resolution details (zero-padded CIK, name matching via SEC EDGAR, private-company outcome), which enriches parameter understanding beyond the schema.

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 leads with concrete user intents and defines the tool as a 'full cross-source profile of a US public company in ONE parallel call.' It explicitly contrasts with chaining single-pack lookups, making it easy to distinguish from siblings like resolve_entity and compare_entities.

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 gives an explicit usage directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also covers edge cases like private companies returning resolved:false and notes that person/place support is coming soon, clarifying when not to use it.

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
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly described as identical, and ask_pipeworx_grounded differs only in grounding. generate_users and generate_by_gender overlap, as do ai_visibility_check/scan_competitor_ai_presence and the several polymarket_* tools that all target edge detection and arbitrage. An agent would struggle to select the correct tool.

Naming Consistency2/5

All names use snake_case, but the pattern is inconsistent: some start with verbs (generate_users, resolve_entity, validate_claim), some are nouns (entity_profile, deep_research, recent_changes), and some are compound noun phrases (ai_visibility_check, pipeworx_feedback, polymarket_arbitrage). There is no predictable verb_noun structure across the set.

Tool Count2/5

33 tools is well above the 'too many' threshold, and the set includes many meta-tools, memory helpers, and niche prediction-market tools. While the broad domain might justify some diversity, the count feels bloated and dilutes the server's focus, especially given the server name suggests only random user generation.

Completeness4/5

For the apparent core domain (data lookups, research, prediction market analysis, subscriptions), the tool surface is quite comprehensive: it covers direct queries, grounded answers, deep research, entity profiles, comparisons, claim verification, discovery, trends, memory, and subscription management. Minor gaps exist (e.g., no direct CRUD for user-generated profiles beyond creation), but overall the feature set feels well covered.

Resources