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Glama

Nhtsa

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent/destructive hints, so the bar rests on added context, and the description clears it easily. It discloses the fan-out behavior, the USPTO PatentsView sunset and soft-fail, the expected empty fda_products result for non-biologics companies, and the meaning of sources_used/sources_failed. It also explains edge-case resolution behavior for names and private companies, which is valuable beyond 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 exceptionally information-dense, with every clause carrying behavioral or usage-relevant content. It front-loads the core purpose and the 'ALWAYS PREFER' guidance, then uses semicolon-separated field lists to keep structure readable. A slightly tighter layout or paragraph breaks could improve scannability, but no sentence is wasted.

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?

There is no output schema, so the description must explain return shape, and it does: it enumerates all major result sections (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) plus resolved/sources_failed fields. It also covers failure modes, empty-section semantics, supported value shapes, and type interchangeability, making the tool fully usable without needing to inspect other resources.

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%, so the baseline is 3; the description earns an extra point by enriching the parameters with concrete examples ('AAPL', '0000320193', 'Moderna'), emphasizing zero-padded CIKs, and clarifying that type and value are interchangeable. It also explains how name resolution works via SEC EDGAR, which is not fully explicit in 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 opens with concrete user phrasings and then states a specific action: produce a 'full cross-source profile of a US public company in ONE parallel call.' It clearly names the resource type, the input options (ticker, CIK, name), and the breadth of sources. It also orients the agent away from chaining single-pack lookups, which differentiates it from more narrowly scoped tools.

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 says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which is strong when-to-use guidance. It also clarifies what happens for private companies (resolved:false with a notes line). However, it does not directly name sibling tools like compare_entities or deep_research or explain when those would be more appropriate, so the exclusion guidance is incomplete.

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
Disambiguation3/5

The descriptions are extraordinarily detailed and do a lot of work to differentiate tools, but there is real functional overlap: three ask_pipeworx variants, six Polymarket/bet tools (bet_research, polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) that all target identifying betting/value opportunities, and overlapping ai_visibility_check vs scan_competitor_ai_presence. A capable agent could navigate it, but misselection risk is high.

Naming Consistency3/5

Mostly snake_case and readable, but the verb/noun placement is inconsistent: verb-first (get_makes, list_subscriptions, resolve_entity, decode_vin) mixes with noun-first (entity_profile, bet_research, pipeworx_trending) and branded prefixes (ask_pipeworx, pipeworx_feedback, polymarket_*). No chaotic camelCase mixing, but no single predictable pattern either.

Tool Count2/5

37 tools is well beyond the heavy threshold, and the server named 'Nhtsa' carries only ~6 vehicle-specific tools while the rest is a general-purpose research platform spanning prediction markets, memory, npm packages, AI-marketing audits, and subscriptions. The scope is overloaded and the name badly misrepresents the content, making the surface feel sprawling rather than focused.

Completeness4/5

For the NHTSA vehicle domain it covers the lookup surface well (makes, models, recalls, complaints, safety ratings, VIN decode), and the broader research platform is genuinely deep with grounding, grounding-with-evidence, discovery, subscription, and memory support. Minor gaps exist (no direct vehicle-make year filtering beyond three fields, USPTO patent APIs are soft-failing), but no dead ends for core workflows.

Resources