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Glama

Gwas Catalog

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 mark this as read-only, open-world, idempotent, and non-destructive, and the description adds substantial behavioral detail: internal cascading lookups, graceful degradation if GLEIF or OpenFIGI is unavailable, explicit `unresolved` reporting, and candidate-returning behavior on ambiguous matches. No contradiction with annotations exists.

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 dense but front-loaded with the core directive ('Use FIRST whenever you have a name but need an ID') and supported types. It contains no filler, but the wall-of-text formatting with many parenthetical caveats makes it harder to scan than a structured layout with bullets would be.

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 carries the full burden of explaining return behavior, and it does: it names returned fields (CIK, ticker, LEI, FIGI, RxCUI), explains `figi_candidates` for ambiguity, documents `unresolved`, and covers source labels and degradation. Nothing an agent needs to understand the tool's results is missing.

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?

Although schema coverage is 100%, the description adds crucial meaning beyond the schema: it reveals that ISIN is accepted as input, explains that bond lookups must use the exact issuer name rather than the full noun phrase, and clarifies how multiple matches are presented. This materially improves correct invocation.

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 and resource: it resolves a user-spoken NAME to canonical/official identifiers required by other tools. It enumerates concrete query examples, supported entity types, and even explains what happens on ambiguous matches, making it easy to distinguish from siblings.

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 explicitly says 'Use FIRST whenever you have a name but need an ID' and gives query patterns for each supported type. It does not, however, name sibling tools to avoid or state explicit when-not-to-use conditions, so it 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.

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TDQS

A3.7/5.0
Disambiguation2/5

The set has distinct clusters, but several tools are near-doppelgangers: ask_pipeworx_beta is explicitly identical to ask_pipeworx, ask_pipeworx_grounded differs only in evidence handling, and deep_research/validate_claim overlap with the same routing pipeline. Prediction-market and company-research tools also blur together despite detailed descriptions.

Naming Consistency4/5

Overall naming is mostly consistent verb-first snake_case (get_snp, resolve_entity, list_subscriptions, compare_entities). It misses a 5 because of noun-led entries (entity_profile, snp_associations) and brand-prefixed outliers (pipeworx_feedback, pipeworx_trending, ask_pipeworx variants).

Tool Count2/5

34 tools is well into the too-many range and the count is not justified by the server's stated GWAS Catalog purpose: only get_snp, snp_associations, and studies_by_trait actually serve that domain, while the rest are a broad Pipeworx/prediction-market/memory/subscription grab bag.

Completeness2/5

For a GWAS Catalog server, only three domain-specific lookups exist: SNP lookup, SNP-to-association, and trait-to-studies. There are obvious gaps such as study-by-accession, association tables per study, trait search, or region/gene-based queries. The generic ask_pipeworx router may paper over this, but the named domain's surface is thin and the unrelated tools don't help.

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