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Glama

Europeana

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?

The description goes well beyond readOnly/openWorld/idempotent annotations by disclosing graceful degradation if GLEIF/OpenFIGI are unavailable, cascading internal lookups, explicit `unresolved` reporting instead of omission, ambiguity handling via `figi_candidates`, and source labelling. No annotation contradiction; readOnlyHint aligns with its lookup-only nature.

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 front-loads the main purpose and 'Use FIRST' guidance, and every block adds substantive behavior. It is long, but justified given the multiple entity types, sources, and edge cases; some examples and parentheticals could be tightened without loss.

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 bears the burden of explaining result behavior, and it does: `unresolved` output, `figi_candidates` for ambiguous matches, RxCUI/pipeworx citation, and graceful enrichment degradation. An agent has enough to call it correctly and interpret likely responses.

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 the schema already documents both parameters (100% coverage), the description adds crucial semantic constraints, especially for `value`: 'Pass the ENTITY NAME ONLY' and warns that trailing security-class words like 'revenue bonds' will fail FIGI matching. It also enriches `type` by tying company vs drug to identity source and accepted input forms.

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 states the precise function: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It names the resources (SEC EDGAR, GLEIF, OpenFIGI, RxNorm) and supported types, making it unambiguous and distinguishable from lookup/search 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 gives an explicit trigger: 'Use FIRST whenever you have a name but need an ID.' It also clarifies type-specific inputs and warns against passing full noun phrases. However, it does not explicitly say when not to use this tool relative to sibling tools like entity_profile or compare_entities, so there is a small exclusion gap.

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

Many tools have overlapping purposes, e.g., three versions of ask_pipeworx for similar tasks, and several research/analysis tools (bet_research, deep_research, entity_profile) with unclear boundaries. The mix of Europeana-specific tools with a broad research toolkit creates confusion.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ai_visibility_check, ask_pipeworx), verb_noun patterns (compare_entities, resolve_entity), and short names (search, record, forget). No clear naming convention is followed throughout the set.

Tool Count2/5

33 tools is excessive for a Europeana-focused server, as only 3 tools (search, record, search_within) are directly related to Europeana. The rest are a broad, unrelated toolkit, making the server feel like a kitchen sink rather than a cohesive collection.

Completeness2/5

The Europeana-specific tool surface is severely incomplete, lacking browse collections, advanced filters, or entity linking. The inclusion of many unrelated tools does not compensate for the gaps in the core domain, resulting in a poorly scoped offering.

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