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Glama

Data Govt Nz

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?

Beyond the read-only/open-world/idempotent annotations, the description discloses degradable enrichment (EDGAR identifiers still return if GLEIF/OpenFIGI are unavailable), ambiguity behavior (returns figi_candidates and asserts nothing), and explicit unresolved-field behavior. It also explains internal endpoint cascading, none of which appears in the 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 front-loaded with natural-language queries and the key 'use FIRST' instruction, and the SUPPORTED TYPES block is well organized. It is dense and long, but nearly every sentence carries new information rather than padding.

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?

For a two-parameter, no-output-schema tool, the description covers return shapes (figi_candidates, unresolved, source-labelled identifiers, RxCUI citation), failure degradation, ownership output, and what not to pass in value. No critical invocation detail appears to be missing.

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?

Although the schema documents both parameters at 100% coverage, the description substantially deepens their semantics: accepted ticker/CIK/ISIN/name input forms, drug brand/generic examples, ISIN-to-LEI behavior, and the crucial bond-issuer-name-only rule for value. This is far beyond a baseline schema-only score.

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 the tool's job precisely: it resolves a user-spoken name to canonical identifiers (CIK, ticker, LEI, FIGI, RxCUI) that other tools require as input. It also says to use it FIRST whenever the agent has a name but needs an ID, which distinguishes it from the identity-consumer sibling tools in the context.

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 phrase 'Use FIRST whenever you have a name but need an ID' gives an explicit triggering condition, and the input instructions cover supported entity types and value formats. It does not name alternative sibling tools or list when-not-to-use conditions, so it falls just short of the top of the scale.

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

B3.1/5.0
Disambiguation2/5

Tools span multiple unrelated domains (NZ open data, prediction markets, npm scanning, AI visibility, etc.). Within each domain, some tools are similar (e.g., multiple polymarket tools, multiple ask_pipeworx variants). The wide scope makes it hard for an agent to know which tool to use.

Naming Consistency2/5

Naming conventions are inconsistent: some use snake_case (ask_pipeworx, deep_research), some use underscore verbs (group_list, package_search), some are descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). No clear pattern.

Tool Count2/5

41 tools is high for a server named 'Data Govt Nz' but includes many unrelated tools (Polymarket, npm, AI visibility). The scope is sprawling; many tools seem extraneous to the core purpose.

Completeness2/5

The NZ open data portion is fairly complete (CRUD for groups, organizations, packages, tags), but other areas have only one or two tools (e.g., npm scanning, AI visibility). The overall surface is incomplete for a coherent domain.

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