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Glama

Connecticut DMV

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  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 indicate safe, read-only, idempotent behavior, and the description adds substantial behavioral context beyond that: graceful degradation of LEI/FIGI enrichment, internal cascading across endpoints, explicit reporting of unresolved identifiers rather than omission, and support for non-equity instruments that never have tickers. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with examples and rich with useful detail, but the middle is a long, run-on passage that bundles ISIN resolution, unresolved-identifier handling, and source labeling into one dense block. It earns its content, but the structure is harder to parse than it should be.

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 burden of explaining return behavior, and it does so thoroughly: identifiers returned per type, source attribution, unresolved-value handling, graceful fallback, and accepted input formats. For a two-parameter resolver, this is complete enough for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description goes well beyond the schema by explaining what each type returns (CIK, ticker, LEI, FIGI, RxCUI, ingredient, brand), how input formats are interpreted, and adding a negative example: passing 'NEW YORK ST DORM AUTH revenue bonds' instead of the issuer name alone would fail. This materially helps an agent construct correct calls.

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 natural-language examples and states a specific verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It also distinguishes itself from siblings by framing itself as the first step before other tools, and the SUPPORTED TYPES detail makes its scope unmistakable.

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?

It gives explicit guidance: 'Use FIRST whenever you have a name but need an ID,' and explains it replaces multiple manual lookups. It does not name specific sibling alternatives or state explicit when-not scenarios, but the context is clear enough that an agent can decide when to invoke it.

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

A3.5/5.0
Disambiguation2/5

Several tools are near-identical in purpose: ask_pipeworx and ask_pipeworx_beta are explicitly the same right now, while ai_visibility_check and scan_competitor_ai_presence overlap heavily. The only DMV-specific tool is otherwise buried among generic research, prediction-market, memory, and subscription tools that an agent would struggle to separate.

Naming Consistency3/5

Most tools use snake_case, but conventions are mixed: some are verb_noun (list_subscriptions, generate_llms_txt), some are bare verbs (remember, forget), some are brand-prefixed nouns (pipeworx_trending, polymarket_edges), and ask_pipeworx lacks a conventional verb pattern. Still readable, but not a cohesive naming scheme.

Tool Count1/5

32 tools is already heavy, but nearly all of them are unrelated to the stated Connecticut DMV scope. The server would be better served by a handful of DMV-focused tools; the current count is an extreme mismatch between name and content.

Completeness1/5

The only DMV tool is ct_dmv_ev_registrations, covering EV registration counts from a single February 2025 snapshot. There is no general vehicle registration lookup, driver licensing, plate/ VIN search, appointment, or form coverage, so the DMV domain is severely incomplete.

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