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Glama

Aviationstack

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?

With read-only, idempotent annotations already present, the description adds substantial behavioral detail: it cascades through multiple endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, explicitly reports unresolved identifiers, and returns `figi_candidates` on ambiguity. These traits go well beyond the annotations and help an agent reason about edge cases.

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 densely packed with necessary detail for a complex two-type resolution tool. It is front-loaded with usage examples and purpose, then organized sections for supported types and fallback behavior. Every sentence adds operational value, though it is verbose enough that a tight edit could 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?

There is no output schema, and the description compensates by explaining what identifiers are returned (CIK, ticker, LEI, FIGI, RxCUI, ingredient, brand), how ambiguity is surfaced (`figi_candidates`), how failures are represented (`unresolved`), and how upstream service outages are handled. An agent has enough information to call this tool correctly and interpret its 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 already 100%, so the baseline is 3. The description adds meaning beyond the schema by clarifying that ISINs resolve to legal entities, explaining exactly how instrument-name mismatches behave, and stressing the need to pass the entity name only for bonds. This is more than the schema already provides.

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 clearly states the tool's specific verb and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It immediately orients with natural language examples and explicitly names two supported entity types, making its purpose specific and distinguishable from profile or comparison tools.

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 says 'Use FIRST whenever you have a name but need an ID,' giving explicit context for when to call this tool. It also notes that it replaces 2-3 manual lookups, but it does not name alternative tools or explicitly state when not to use 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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, and deep_research all routing queries, and the polymarket family (arbitrage, edges, edge_tracker, fill_risk) covering similar ground. While descriptions are detailed, an agent could easily select the wrong tool.

Naming Consistency2/5

Tool names mix bare nouns (airlines, airports, flights) with verb phrases (compare_entities, resolve_entity) and standalone verbs (remember, forget), with no consistent verb_noun pattern. Names like ask_pipeworx_beta and scan_competitor_ai_presence are internally inconsistent with the rest.

Tool Count2/5

37 tools is far beyond the typical scope for a single server, and many are unrelated to the aviation theme, suggesting a lack of focus. The core aviation functionality only accounts for 6 of the tools.

Completeness3/5

The aviation tools (airlines, airports, flights, routes, cities, countries) cover basic lookups, but advanced operations like delay statistics or aircraft data are absent. The unrelated tools do not fill these gaps, and the overall surface feels shallow for a server claiming to be an Aviationstack.

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