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countries

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

A4.7/5.0
Behavior5/5

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

Even though annotations already indicate readOnly, idempotent, and non-destructive behavior, the description adds substantial operational detail: internal cascading through multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI is unavailable, explicit `unresolved` reporting, `figi_candidates` on ambiguous matches, source labelling for identifiers, and non-equity coverage via FIGI. This gives the agent realistic expectations beyond any structured fields.

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 well-structured: it opens with example user phrasings, then directly states the rule 'Use FIRST whenever you have a name but need an ID', and groups the remaining detail under SUPPORTED TYPES. The caveats are dense and relevant, though the length and interleaving of many identifier-source details keep it from being maximally concise.

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 has no output schema, the description does an excellent job covering what an agent needs to know: what identifiers are returned, how ambiguity is handled, how unresolved identifiers are represented, the acceptable input patterns, and how degradation behaves. The company and drug branches are both fully described, and edge cases like ISIN-to-LEI map and non-ticker instruments are covered.

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 description coverage is 100%, but the description significantly enriches parameter meaning. For `value`, it specifies 'entity name only', exact issuer formatting, and warns against trailing security-class words like 'revenue bonds'. For `type`, it expands the enum into concrete identifier behaviors per type (CIK/ticker/LEI/FIGI for company vs RxCUI/brand/ingredient for drug). This far exceeds the baseline schema-only understanding.

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 clearly states the tool's purpose: resolving a user-spoken NAME into canonical/official identifiers that other tools require. It names concrete verbs and use cases ('What's the ticker for...', 'resolve a user-spoken NAME') and differentiates itself from siblings by asserting 'Use FIRST whenever you have a name but need an ID.' Supported entity types and identifier systems are explicitly enumerated.

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 provides strong usage context: 'Use FIRST whenever you have a name but need an ID' and notes it replaces 2-3 manual lookups. It clearly explains what inputs are accepted and gives detailed formatting constraints for the value parameter. However, it does not name sibling tools as explicit alternatives or state when not to use this tool over them, so it falls just 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

B3.4/5.0
Disambiguation2/5

The tool set is a mix of country lookups and many unrelated Pipeworx tools (e.g., prediction markets, memory, subscriptions). While the country-specific tools are distinct, the overall set is a hodgepodge, making it difficult for an agent to determine which tools are relevant to a given task.

Naming Consistency2/5

Naming conventions are mixed: some use verb_noun (search_countries), some noun_verb (countries_by_currency), some single verbs (forget, recall), and some phrases (ai_visibility_check, ask_pipeworx). No consistent pattern is followed.

Tool Count1/5

35 tools is excessive for a server named 'countries'. Only about 6-7 tools are actually related to countries; the rest are unrelated (Pipeworx services, prediction markets, etc.), creating an extreme mismatch between the server name and its content.

Completeness2/5

For the country domain, the tool set includes search and lookups by code/currency/language/region but lacks basic CRUD, comparisons, maps, or sorting. Additionally, the presence of many unrelated tools dilutes completeness for the stated purpose.

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