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Glama

Lyrics Ovh

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

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses soft-fail behavior for USPTO PatentsView, GDELT→GNews fallback, empty sections meaning 'no data' rather than a bug, and resolved:false behavior for private companies. It also clarifies that absent FDA products is expected for non-biologic companies, which is valuable behavioral context.

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 segment earns its place: usage triggers, source list, return fields, failure semantics, and fallback behavior. It is front-loaded with user intents and the 'ALWAYS PREFER' instruction, though it remains a dense single block rather than being visually structured.

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?

Despite having no output schema, the description enumerates the main returned fields (cik, company_name, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI, sources_used/sources_failed) and explains empty and failure cases. An agent has enough information to call this tool correctly and interpret its results.

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?

The input schema already covers both parameters thoroughly with examples like 'AAPL', zero-padded CIK, and 'Moderna', and states that 'company' and 'ticker' behave identically. The description largely restates this rather than adding new parameter-level meaning, so it earns the baseline for high schema coverage.

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?

Opening with concrete user phrasings and 'full cross-source profile of a US public company in ONE parallel call' gives a specific verb, resource, and scope. It also differentiates itself from single-pack SEC/XBRL/news lookups by explicitly saying it should be preferred over chaining them.

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?

The description explicitly states when to use: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also specifies accepted inputs and what happens for private companies, though it does not explicitly contrast against sibling tools like compare_entities or resolve_entity.

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
Disambiguation3/5

Several tools have overlapping purposes. ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route to the same 5,564 tools, differing only in grounding behavior. suggest and lyrics both look up music but in different ways; suggest is broader while lyrics is exact. The core tools are distinct, but the multiple pipeworx variants and music tools create ambiguity.

Naming Consistency2/5

Naming is highly inconsistent. Most tools use snake_case (ask_pipeworx, entity_profile, compare_entities), but several use verb phrases (generate_llms_txt, scan_competitor_ai_presence) and some use short nouns (lyrics, suggest). There's no consistent verb_noun pattern; 'ask_pipeworx' variants mix imperative with domain words, and 'recall'/'remember' are verbs without objects.

Tool Count4/5

With 33 tools, the server covers a wide domain (company research, prediction markets, news, weather, lyrics, memory, subscriptions, etc.). While this is many tools, each has a specific purpose and the variety matches the stated 'universal router' / 'thousands of data sources' value prop. It could be trimmed slightly, but the count is justified by the breadth.

Completeness5/5

The tool surface is remarkably complete for its stated purpose: unstructured lookup (ask_pipeworx), grounded verification (ask_pipeworx_grounded), deep multi-source research (deep_research), entity profiles, comparisons, change feeds, arbitrage scanning, memory, subscriptions, and even feedback/governance tools. It covers all common patterns in data retrieval and has distinct tools for edge cases, making it hard to find obvious gaps.

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