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Glama

Always Seven

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes well beyond this: it discloses the multi-source fan-out, the soft-fail on the PatentsView sunset, the meaning of empty sections, the resolved:false behavior for private companies, and the sources_used/sources_failed semantics. No contradiction exists between annotations and text.

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 a single dense paragraph, but it is front-loaded with triggers and the 'ALWAYS PREFER' directive, then systematically enumerates the fan-out sources and return sections. There is no fluff, but the length could be eased with bullet points or sub-headings without losing information. It is appropriately sized for the tool's complexity.

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 carries the full burden of explaining the return value shape, and it does so exhaustively: it lists every section (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI), explains empty section semantics, sources_used/sources_failed, and the private-company behavior. Nothing an agent needs to call it correctly is omitted.

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 both parameters already have detailed descriptions. The description adds meaningful extra nuance: that `type` values are interchangeable and `value` can be a ticker, CIK, or name in either case. This goes beyond the schema's per-parameter text, so a 4 is warranted.

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 ('profile') and a well-scoped resource ('full cross-source profile of a US public company'), and the first line lists natural-language triggers. It explicitly differentiates itself from single-pack lookups, making its purpose unmistakable and distinct from sibling tools like deep_research or 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?

The description contains an explicit directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also covers the input variants (ticker, CIK, name) and the private-company edge case with resolved:false, giving clear when-to-use and implied when-not-to-use guidance. Alternatives are named in the sibling list and the 'prefer over' statement effectively routes around them.

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

A3.8/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying 5,708-tool catalog, and ask_pipeworx_beta is explicitly identical today. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also has fuzzy boundaries — both bet_research and polymarket_edges claim 'should I bet on X', and discover_tools versus suggest_questions both serve discovery. Despite very detailed descriptions, an agent would frequently struggle to pick the correct tool.

Naming Consistency3/5

The naming is mostly snake_case and readable, with coherent micro-families (polymarket_*, pipeworx_*, ask_pipeworx variants, remember/recall/forget). However, patterns are mixed: verb_noun (compare_entities, resolve_entity, generate_llms_txt) sits alongside noun-led names (entity_profile, recent_alerts, pipeworx_trending), and entity-related tools use three different conventions (compare_entities, resolve_entity, entity_profile). It's consistent within families but not across the full set.

Tool Count2/5

32 tools is well past the 25-tool threshold for a coherent set, and the scope is a scattered grab bag: a joke RNG, an AI-visibility probe, a 5,708-tool data router, prediction-market arb analytics, key-value memory, subscriptions, npm dependency scanning, llms.txt generation, and a feedback channel. Some of these are arguably platform additions rather than core tools, but as presented the count feels bloated and unfocused.

Completeness3/5

The major workflow areas are well covered — data lookup has routing, grounded mode, deep research, entity resolution, comparisons, profiles, and change feeds; memory and subscriptions each have full lifecycle coverage. However, the server repeatedly references pipeworx:// citation URIs as fetchable yet provides no record-fetching tool, and the diffuse purpose makes it hard to assess what 'complete' even means. Notable gaps exist around citation resolution and execution of the arbitrage signals the Polymarket tools generate.

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