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Glama

Texas 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

A4.7/5.0
Behavior5/5

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

Annotations already cover readOnly, openWorld, idempotent, and non-destructive behavior, so the description goes well beyond that by disclosing internal cascading through multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, and the explicit handling of unresolved identifiers under `unresolved`. This is valuable behavioral context a caller would not otherwise know.

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 and dense, but the length is justified by the tool's dual entity types and multiple lookup sources. It is front-loaded with query examples and the core directive, though some nested parentheticals make scanning harder than it could 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 and two complex entity types, the description covers return fields, provider sources, input edge cases, and failure behavior. An agent has nearly everything needed to decide when to call it and how to format both `type` and `value` correctly.

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?

Schema coverage is 100%, but the description adds substantial parameter meaning: it lists accepted formats (ticker, CIK, ISIN, company name), distinguishes drug brand vs generic input, and gives a critical warning about bond issuer names needing exact printed name without trailing security-class words. This goes far beyond the schema's basic descriptions.

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 queries and states a precise verb-resource relationship: resolving user-spoken names to canonical/official identifiers that other tools require. It enumerates supported entity types and distinguishes this from siblings like entity_profile or validate_claim by framing it as the ID-lookup entry point.

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 explicitly instructs 'Use FIRST whenever you have a name but need an ID,' which gives the agent a clear routing rule. It does not name alternative sibling tools or provide explicit 'when not to use' conditions, but the main usage context is unmistakable.

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 occupy nearly interchangeable roles: ask_pipeworx and ask_pipeworx_beta are explicitly identical, while ask_pipeworx_grounded, deep_research, and validate_claim all route similar factual queries. discover_tools/suggest_questions and bet_research/polymarket_edges similarly overlap, so an agent needs to read long descriptions to avoid misselection.

Naming Consistency3/5

All names are readable lowercase snake_case, but the conventions are mixed: imperative verb_noun names (list_subscriptions, validate_claim) sit alongside noun phrases (polymarket_edges, recent_alerts), bare verbs (forget, subscribe), and variant suffixes (ask_pipeworx_beta/grounded). It is not chaotic, but there is no single predictable naming pattern.

Tool Count2/5

Thirty-two tools is far more than the apparent Texas DMV scope supports: only tx_dmv_vehicle_registrations is DMV-related, and the rest are Pipeworx platform, prediction-market, memory, and unrelated utility tools. The count is excessive for the server's stated name and purpose.

Completeness1/5

The Texas DMV surface is severely incomplete: a single statewide registration-count tool covering fiscal years 2001-2021, with no title/registration transactions, VIN lookup, driver services, county/ZIP breakdowns, or current data. The tool's own description references a California DMV companion that is not present, leaving obvious gaps for any realistic DMV workflow.

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