Skip to main content
Glama

Gtin

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 declare readOnly/openWorld/idempotent, and the description adds substantial behavioral context: it cascades through multiple lookup endpoints, returns figi_candidates when ambiguity exists, reports unresolved identifiers explicitly, and degrades gracefully when GLEIF/OpenFIGI are unavailable while still returning EDGAR identifiers. This goes well beyond what annotations alone convey.

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 important guidance is front-loaded and organized with 'SUPPORTED TYPES' sections. The opening example list is somewhat redundant, and the parenthetical chains make it heavier than ideal, but nearly every sentence contributes operational value.

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 tool with two required parameters and no output schema, the description is remarkably complete: it covers input formats, supported types, enrichment sources, ambiguity handling, unresolved results, degradation behavior, and even edge cases like non-US issuers. An agent has everything it needs to invoke the tool 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?

Even though schema coverage is 100%, the description enriches both parameters significantly. It explains type-specific behavior, gives accepted formats for value (ticker, CIK, ISIN, company name, drug brand/generic), and warns emphatically to pass only the entity name, not the full noun phrase — critical detail absent from 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 and a crisp statement: resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. It enumerates supported entity types (company, drug) and specific identifier outputs, making the tool's job unambiguous and distinct from siblings like entity_profile.

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 explicitly says 'Use FIRST whenever you have a name but need an ID,' giving a clear trigger condition. It also explains how this tool replaces 2-3 manual lookups, but it does not explicitly name alternative tools or state when not to use it, so the guidance falls just short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Gtin toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve a query-routing/research function, with ask_pipeworx and ask_pipeworx_beta being currently identical. The five polymarket_* tools also share boundaries, making it genuinely ambiguous which one to pick for a given betting question.

Naming Consistency2/5

The set mixes verb-style names (forget, recall, subscribe, unsubscribe), noun-style names (entity_profile, recent_changes, bet_research), and brand-prefixed families (ask_pipeworx*, polymarket_*). There is no single verb_noun pattern, and conventions differ across families even though individual families are internally consistent.

Tool Count2/5

With 33 tools, the count is high, and the server is named 'Gtin' yet only two tools relate to barcodes/GTIN — the rest form a sprawling data-research and prediction-market toolkit. Many tools could be consolidated (e.g., the ask_pipeworx variants, the polymarket suite), so the surface feels heavier than its core purpose requires.

Completeness4/5

For the actual domain revealed by the tools — multi-source structured data lookup, entity profiling, prediction-market analysis, and agent memory — the surface is quite complete: it covers query routing, grounded verification, comparisons, research, subscriptions, memory, and feedback loops. However, given the server name 'Gtin', the barcode domain is severely under-covered (only validation and check digit, no lookup or product data), which prevents a perfect score.

Resources