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Glama

Ygoprodeck

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

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds rich context: graceful degradation when GLEIF/OpenFIGI are down, cascading internal lookups (replacing 2-3 manual lookups), explicit 'unresolved' reporting instead of omission, and the behavior of asserting nothing on ambiguous matches. This goes well beyond the safety profile conveyed by annotations.

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 dense, with no filler. It front-loads the core purpose and usage guidance, then systematically covers types and caveats. While lengthy, every sentence contributes unique information, so the length is justified. It could be slightly streamlined, but it's structured well with clear segmentation.

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?

Given the tool's complexity (multiple identifier sources, two distinct entity types, graceful degradation, ambiguous-match handling) and the absence of an output schema, the description covers all necessary aspects: what inputs to provide, how outputs are labeled (unresolved, figi_candidates), and the overall resolution flow. An agent has everything needed to invoke it correctly and interpret results.

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?

Schema coverage is 100%, so each parameter is described, but the description significantly expands on the `value` parameter: it provides examples, distinguishes between ticker/CIK/name for company, explains issuer-name formatting for bonds (e.g., 'NEW YORK ST DORM AUTH' not '... revenue bonds'), and clarifies the expected granularity. This adds critical detail not present in 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 states a specific verb ('resolve'), resource ('canonical/official identifiers'), and the user's intent (name-to-ID). It lists concrete query examples and explicitly positions itself as the first tool to use when a name is known but an ID is needed, distinguishing it from other tools like entity_profile that likely operate on already-resolved entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains when to expect a specific output (figi_candidates for ambiguous matches), what input formats are accepted, and when NOT to pass extra words (trailing security-class words for bonds). It implicitly steers away from tools that require IDs as input.

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

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but the cluster of Pipeworx tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar, causing potential confusion. The memory tools (remember, recall, forget) also add some overlap.

Naming Consistency3/5

All tool names use lowercase with underscores, which is consistent. However, the similar Pipeworx tools have confusingly similar names (ask_pipeworx vs ask_pipeworx_grounded vs ask_pipeworx_beta), and the naming does not clearly distinguish their differences.

Tool Count2/5

With 34 tools, the set is too large for a server ostensibly focused on Yu-Gi-Oh! cards. The majority of tools are unrelated domain-agnostic data tools, making the count feel bloated and unfocused.

Completeness2/5

The Yu-Gi-Oh! card tools are limited to lookup and search, missing obvious operations like creating or updating cards. The unrelated data tools, while many, do not form a coherent set for a single purpose, leaving gaps in both directions.

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