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Glama

airquality

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, openWorld), the description discloses important behavioral traits: ambiguity handling ('when a name matches more than one instrument it asserts nothing and returns figi_candidates'), explicit unresolved reporting ('an identifier that could NOT be resolved is stated explicitly under unresolved'), graceful degradation when GLEIF/OpenFIGI is unavailable, and internal cascading across lookup endpoints. It also explains the ISIN-to-LEI resolution path and non-equity coverage, giving the agent a full model of how resolution may behave.

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 appropriately structured with a front-loaded purpose, example phrases, SUPPORTED TYPES breakdown, and specific behavioral notes. Each sentence carries useful information for a complex tool, so the length is earned. It loses one point because the density could overwhelm an agent looking for a quick summary, and a brief 'what this returns' line at the top would improve scannability.

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 full burden of explaining return behavior, and it does so exceptionally: it lists identifiers returned, labels by source, reports unresolved values explicitly, details ambiguity handling (figi_candidates), and covers graceful degradation. For a tool with this complexity, two parameters, and no output schema, everything an agent needs to call it correctly and interpret results is present.

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 description coverage is 100% and the schema already provides strong guidance for both parameters, including the entity-name-only warning. The description adds meaning beyond the schema by mentioning additional acceptable inputs (ISIN), explaining cross-source resolution semantics (CIK + ticker + LEI + FIGI), and specifying what identifiers are returned for each type. This is more than the baseline 3 for fully covered schemas, but not maximal since some detail duplicates 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 ('What's the ticker for…' / 'find the CIK for…') followed by a precise statement of purpose: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It names the resource classes (company, drug) and the specific identifiers (CIK, LEI, FIGI, RxCUI), and the directive 'Use FIRST whenever you have a name but need an ID' clearly differentiates it from sibling tools like entity_profile and compare_entities.

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 gives explicit usage guidance: 'Use FIRST whenever you have a name but need an ID' and clarifies that this tool replaces 2-3 manual lookups. It also explains supported input forms and the entity-name-only rule for bonds. However, it does not explicitly name alternatives or state when NOT to use this tool (e.g., if you already have an identifier), leaving that inference to the agent.

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
Disambiguation3/5

Many tools have overlapping purposes, especially multiple ask_pipeworx variants and numerous Polymarket tools. Detailed descriptions help, but an agent could still confuse similar tools like ask_pipeworx and ask_pipeworx_beta.

Naming Consistency2/5

Tool names use a mix of styles: some are descriptive (get_air_quality), some use proprietary prefixes (pipeworx_feedback), and others are arbitrary (bet_research). No consistent verb_noun pattern.

Tool Count1/5

33 tools is excessive for a server named 'airquality', which only has 2 air-quality-specific tools. The count is appropriate for a general data platform, but mismatched with the server name.

Completeness1/5

For the air quality domain, the tool set is severely incomplete, missing historical data, pollution sources, and health recommendations. The overall set covers many other domains, but fails to address the server's apparent focus.

Resources