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Glama

Delaware DMV

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

A4.8/5.0
Behavior5/5

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

The description goes far beyond the annotations, explaining the parallel fan-out across multiple sources, fallback behavior (GDELT→GNews, soft-fail for patents), empty-section semantics ('no data, not a bug'), private-company resolution behavior, and output details. It fully discloses the tool's operational traits.

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 sentence adds operational or semantic value. It is front-loaded with the purpose and usage priority, followed by source enumeration and edge-case handling. Slightly dense, but appropriate 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?

Even though there is no output schema, the description thoroughly explains return values (cik, company_name, recent_filings, fundamentals, sources_used/sources_failed) and error handling (resolved:false, notes). It is complete enough for an agent to invoke the tool correctly and interpret results.

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 adds extra value by clarifying that type is interchangeable, names resolve via SEC EDGAR, and value can be a ticker, CIK, or company name with specific examples. This is a meaningful enrichment beyond 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 states a specific verb and resource: it produces a full cross-source profile of a US public company in one parallel call. It is clearly distinguishable from sibling tools like compare_entities, resolve_entity, and deep_research by its unique scope and output.

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?

Explicit guidance is given: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also shows example user queries that should trigger this tool, providing clear when-to-use context without ambiguity.

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
Disambiguation2/5

Several tools are effectively duplicates or near-duplicates: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ai_visibility_check and scan_competitor_ai_presence overlap heavily, and the polymarket_arbitrage/polymarket_edges/polymarket_fill_risk cluster has fuzzy boundaries. Even within the DMV subset, de_dmv_ev_adoption and de_dmv_vehicle_registrations both answer overlapping EV-count questions.

Naming Consistency2/5

The de_dmv_* tools follow one snake_case pattern, but the rest of the set mixes bare nouns, brand-prefixed verbs, and generic names (entity_profile, remember, generate_llms_txt, ask_pipeworx_beta). There is no consistent verb_noun or domain-prefix convention across the 36 tools.

Tool Count2/5

36 tools is over the threshold where a tool set becomes hard to navigate, and most of them have nothing to do with a Delaware DMV server. Only five tools are DMV-related; the rest are a general-purpose Pipeworx data, memory, and prediction-market toolkit, which makes the set feel bloated and mis-scoped.

Completeness2/5

For a server named Delaware DMV, the surface is missing core DMV capabilities like driver licenses, vehicle titling, registration renewals, appointments, or fee lookups. The five de_dmv_* tools cover only EV adoption, rebates, charger rebates, crash stats, and registration counts, leaving obvious domain gaps.

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