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Glama

Eurostat

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?

Even with annotations already declaring readOnly/openWorld/idempotent behavior, the description adds substantial behavioral context: ambiguous matches return figi_candidates without asserting, unresolved identifiers are explicitly listed under 'unresolved' rather than omitted, and LEI/FIGI enrichment degrades gracefully when upstream sources are unavailable. It also clarifies that a non-ticker instrument can still resolve via name search, which is non-obvious.

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 and long, but it is front-loaded with the core purpose and trigger. Each clause earns its place by adding behavioral or semantic precision. It is not maximally concise, but the length is justified by the tool's multi-source, multi-entity-type complexity.

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?

There is no output schema, so the description must convey return semantics, and it does. It names the key output identifiers (CIK, LEI, FIGI, RxCUI), the ambiguity behavior (figi_candidates), the unresolved behavior, source labeling, and graceful degradation. For a tool with this complexity, the description covers what an agent needs 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 the baseline is 3, but the description goes far beyond the schema. It details accepted input formats (ticker, CIK, ISIN, name for company; brand/generic for drug), gives concrete examples, and includes crucial nuance like passing only the issuer name and not trailing security-class words for FIGI lookups. 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 very specific purpose: resolving user-spoken names to canonical/official identifiers. It provides numerous concrete query examples and explicitly differentiates the tool's role from other tools by saying 'Use FIRST whenever you have a name but need an ID.' This makes the tool's function unambiguous and distinct from sibling tools like entity_profile or validate_claim.

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 gives an explicit trigger condition ('Use FIRST whenever you have a name but need an ID') and elaborates on supported entity types and graceful degradation. However, it does not explicitly name sibling alternatives or state when NOT to use this tool, so it stops short of full exclusion guidance.

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 is heavily overlapped: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all serve similar factual-lookup purposes with fuzzy boundaries. The Polymarket tools and visibility tools also overlap substantially, making tool selection genuinely ambiguous despite long descriptions.

Naming Consistency3/5

All names are lowercase snake_case, which is internally consistent, but there is no predictable verb_noun pattern: verb styles vary wildly (ask, get, list, search, recall, remember, forget, subscribe). The 'ask_pipeworx_beta' suffix also breaks naming convention.

Tool Count1/5

34 tools is far too many for a server named Eurostat, and only 3 of them (get_dataset, list_datasets, search_datasets) actually serve Eurostat data. The rest is a sprawling generic Pipeworx utility surface including prediction markets, memory, subscriptions, visibility checks, and dependency scanning, which is a severe scope mismatch.

Completeness2/5

For the Eurostat-specific portion, search/list/get covers basic dataset retrieval, but the server's broader surface is a grab-bag of unrelated capabilities with no coherent domain. The true domain is unclear, and the Eurostat side lacks deeper operations like metadata lookup or bulk/time-series expansion.

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