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Shopify

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?

Annotations already mark the tool as read-only, idempotent, and non-destructive, and the description adds substantial behavioral context beyond that: it cascades through multiple endpoints, returns `figi_candidates` on ambiguous matches, explicitly reports `unresolved` identifiers, labels identifiers with their source, and degrades gracefully if LEI/FIGI enrichment sources are unavailable.

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 most important guidance is front-loaded with 'Use FIRST...' and the supported types are organized into a clear section. The description is dense and uses long nested parentheticals that could be restructured as bullets, but nearly every sentence adds useful operational detail.

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 to lean on, the description compensates by naming key output behaviors and fields: `figi_candidates`, `unresolved`, source-labelled identifiers, drug RxCUI citation format, and graceful degradation. Given the tool's complexity and two-parameter input, this is complete enough for an agent to call it correctly and interpret ambiguous 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?

The schema already covers both parameters, and the description adds meaningful semantics beyond it: accepted input formats per type, ISIN-to-LEI resolution behavior, exact-issuer formatting for bond names, and brand/generic drug examples. It goes well beyond the schema's enum and value descriptions.

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 gives concrete user-phrase examples and states a specific verb plus resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It names two supported types and explicitly positions itself as the prerequisite ID lookup step, clearly distinguishing it from sibling profile and research 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?

It provides an explicit directive—'Use FIRST whenever you have a name but need an ID'—and notes it replaces 2-3 manual lookups. However, it does not name sibling tools or state when-not-to-use conditions, such as when the user already has an identifier and needs profile enrichment instead.

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

B3.3/5.0
Disambiguation2/5

Many tools overlap in purpose, especially the Pipeworx data retrieval tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). The Shopify-specific tools are distinct, but the overall set is a confusing mix of domains, making it hard for an agent to select the right tool.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use snake_case (ai_visibility_check, bet_research), others use underscores in various patterns (generate_llms_txt, scan_competitor_ai_presence). The Shopify tools use a shopify_ prefix, but the rest follow no uniform scheme, making it unpredictable.

Tool Count1/5

35 tools is far too many for a server named 'Shopify'. Only 5 tools are Shopify-specific; the rest are general-purpose data tools (Pipeworx, Polymarket, memory, etc.). This severe scope mismatch makes the tool count inappropriate and overwhelming for the intended domain.

Completeness2/5

As a Shopify server, the tools cover only basic read operations (list/get products, orders, customers), missing crucial write operations (create, update, delete) and other Shopify features (webhooks, inventory, etc.). The general tools cover many domains but are not integrated into a coherent Shopify workflow.

Resources