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Glama

Zoom

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

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description reveals graceful degradation when GLEIF/OpenFIGI are unavailable, explicit unresolved identifiers, figi_candidates on ambiguity, and internal cascading lookups. This goes well beyond what annotations alone communicate.

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 dense and front-loaded with the use-first directive, but the long parentheticals about LEI/FIGI and bond instruments make it hard to scan. Much of that detail is valuable, but the structure could be tighter.

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 still explains return behavior well: identifiers labelled by source, unresolved items listed explicitly, figi_candidates on multi-match, and drug results including RxCUI/ingredient/brand/citation. It covers inputs, edge cases, and degradation, making the tool fully actionable.

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?

The schema already documents type and value clearly, but the description adds accepted input forms (ticker, CIK, ISIN) and crucial boundary guidance for bond issuer names, such as excluding trailing security-class words. This meaningfully reduces invocation errors beyond 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 natural-language examples and states it resolves a name to canonical identifiers required by other tools, listing CIK, ticker, LEI, RxCUI, and FIGI. It also distinguishes itself from siblings by declaring 'Use FIRST whenever you have a name but need an ID.'

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 an explicit decision rule ('Use FIRST whenever you have a name but need an ID') and notes it replaces 2-3 manual lookups. It does not name sibling alternatives or state when to prefer entity_profile/compare_entities, so exclusions are mostly implicit.

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

The set mixes two entirely different domains: 4 Zoom tools and 31 Pipeworx/prediction-market tools. Within the Pipeworx side, ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions overlap heavily, as do the five polymarket_* tools. An agent could easily select the wrong variant despite the long descriptions.

Naming Consistency2/5

The Zoom tools follow a clean zoom_* pattern, and there are subfamilies like ask_pipeworx_* and polymarket_*, but the overall set is a mix of snake_case verbs, bare nouns, and inconsistent styles (bet_research, entity_profile, generate_llms_txt, list_subscriptions, pipeworx_feedback, validate_claim). No single predictable convention governs the server's tool names.

Tool Count2/5

35 tools is heavy for any server, and the vast majority are unrelated to the server's declared 'Zoom' purpose. Only 4 of 35 tools actually concern Zoom, making the count both bloated and mismatched. A focused Zoom server would need far fewer tools; a Pipeworx data server would need a different name.

Completeness2/5

For a Zoom server, the surface is critically incomplete: only read-only list/get operations exist for meetings, recordings, and the current user, with no create, update, delete, or invite functionality. The Pipeworx side is comparatively rich and complete, but that does not serve the Zoom domain implied by the server name, so significant gaps remain for the apparent purpose.

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