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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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds substantial behavioral context beyond that: parallel fan-out across sources, soft-fail behavior for sunset APIs, the meaning of sources_used/sources_failed, empty-section semantics, and resolved:false for private companies. 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 section carries information: examples, preference guidance, source fan-out, return fields with caveats, and input semantics. It is dense rather than wasteful, though a bit of line-break structure could 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?

Given there is no output schema, the description carries the full burden of explaining return values and does so thoroughly: it lists cik, company_name, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, and sources_used/sources_failed. It also handles edge cases like private companies, empty data, and API sunset behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description still adds meaningful value by explaining that type 'company' and 'ticker' are interchangeable, that value can be a ticker, zero-padded CIK, or company name, and that names resolve via SEC EDGAR's company-name match. This goes well beyond the schema's field descriptions.

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 states a specific verb and resource: it builds a 'full cross-source profile of a US public company' in one parallel call. It lists concrete user phrasings ('Tell me about X', 'research Acme') and explicitly differentiates itself from chaining single-pack SEC/XBRL/news lookups.

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?

The description is explicit about when to use this tool: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also covers input variations (ticker, CIK, name) and the edge case of private companies, making the selection criteria and invocation behavior clear.

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, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle natural-language data queries, with ask_pipeworx_beta explicitly duplicating ask_pipeworx. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research) also heavily overlaps, and ai_visibility_check is a single-entity version of scan_competitor_ai_presence. An agent would frequently be unable to tell which tool to select.

Naming Consistency2/5

All names are snake_case, but the pattern is inconsistent: some are verb_noun (generate_llms_txt, resolve_entity), some are bare verbs (forget, recall, subscribe), and several are noun-first domain names (polymarket_edges, pipeworx_trending, entity_profile). There is no uniform verb convention, and the mix makes it hard to predict what a tool does from its name.

Tool Count2/5

At 33 tools, this is well above the 'heavy' threshold and includes several near-duplicates: three ask_pipeworx variants and six polymarket_* tools. While the underlying platform is broad, this meta-layer could be consolidated to 15-20 tools without losing capability.

Completeness4/5

The core data-query workflow is well covered: ask, deep research, entity profile, compare, validate, resolve ID, and search inside documents. The memory lifecycle (remember/recall/forget) and subscription lifecycle (subscribe/list/recent_alerts/unsubscribe) are also complete. However, the set includes unrelated utilities (generate_paragraphs, scan_dependency, generate_llms_txt) that don't belong to the main data domain, and there is no direct tool to execute a raw discovered tool by name.

Resources