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Scb Se

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

A4.6/5.0
Behavior5/5

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

Annotations already mark it readOnly, idempotent, and non-destructive, but the description goes far beyond: it discloses the fan-out across many sources, soft-failure for the PatentsView sunset, that an empty fda_products section is 'not a failure', the existence of sources_used/sources_failed, and the resolved:false behavior for private companies. These are behavioral traits an agent could not infer from the schema alone.

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 exceptionally dense; every clause adds operational detail that matters for a no-output-schema tool. It is front-loaded with the core purpose and preference guidance, then systematically walks through return sections and edge cases. Some repetition of the input-cardinality point exists ('same value shapes' appears in both description and schema), but overall no filler.

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?

Without an output schema, the description carries full burden for explaining return values, and it does: cik, resolved_from/to, recent_filings with URIs, fundamentals with sort order, patents sunset, federal_contracts, fda_products expectations, hiring, news fallback, LEI, and sources_used/sources_failed. Edge cases (private companies, empty sections, soft-fails) are explicitly handled. An agent has enough to set expectations and interpret results correctly.

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?

Schema coverage is 100% and already explains type and value, but the description adds meaningful semantic context: zero-padded CIK examples ('0000320193'), that names resolve via SEC EDGAR company-name match, and that type='company' and type='ticker' are interchangeable ('both accept the same value shapes'). This enriches the raw schema information.

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 example phrasings then states a specific resource: 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes itself from the alternative of chaining single-pack SEC/XBRL/news lookups, which separates it from sibling tools. This is a specific verb+resource+scope statement.

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 an explicit preference rule: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies behavior for private companies (returns resolved:false with notes) rather than failing. It does not explicitly name alternative sibling tools like deep_research or resolve_entity, leaving some routing ambiguity for broader vs. narrower research tools.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but several overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (only differing in verification/depth), and polymarket_edges / polymarket_arbitrage / polymarket_edge_tracker / polymarket_fill_risk / polymarket_kalshi_spread all target similar prediction-market signals, which could cause mis-selection without careful reading.

Naming Consistency4/5

Most names follow a clear verb_noun pattern (resolve_entity, query_table, remember, recall, forget, subscribe, unsubscribe, validate_claim, compare_entities, search_within), but there are exceptions like ai_visibility_check (adjective_noun), generate_llms_txt (verb_noun with dot), and several polymarket_* names that are fine but inconsistent with the snake_case verb-first convention.

Tool Count5/5

35 tools is a large but reasonable surface for a broad data/serach platform covering company financials, economics, prediction markets, memory, subscriptions, and discovery. The count is justified by the wide domain, and the set is not bloated with trivial duplicates.

Completeness4/5

The surface covers core CRUD for entities (resolve, profile, compare, search, query) and memory (remember/recall/forget), plus subscriptions and meta-tools. Minor gaps: no explicit tool for updating/creating entities (understandable for a read-only data service), and no tool for listing all available table schemas beyond discovery (subjects covers this). Overall strong coverage.

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