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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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description goes well beyond that: it explains the internal cascade ('cascades through several lookup endpoints internally'), the graceful degradation when GLEIF/OpenFIGI is unavailable, the behavior of returning figi_candidates when ambiguous, and the explicit listing of unresolved identifiers. It also notes that it replaces 2-3 manual lookups, giving agents a mental model of the operation's cost and behavior. No contradiction with 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 every sentence earns its place. It front-loads the purpose with concrete examples, then dives into type-specific details. The structure uses clear enumerations ('SUPPORTED TYPES') and explicit warnings. It is not excessively verbose for the complexity it covers, though it could arguably be tightened. A 4 reflects that it is appropriately sized for the tool's richness.

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 no output schema, the description must explain what the agent gets back. It does: identifiers labelled by source, unresolved items listed under 'unresolved', figi_candidates for ambiguity, and the fallback behavior when enrichment services fail. It also covers edge cases like ISIN resolving to the issuer and non-equity instruments. Nothing an agent needs to decide when to call and what to expect 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?

Schema coverage is 100% for both parameters, but the description adds extensive meaning beyond the schema. For 'type', it enumerates the two enum values and what each resolves (company: CIK/ticker/LEI/FIGI; drug: RxCUI/ingredient/brand). For 'value', it explains accepted formats (ticker, CIK, ISIN, name) and explicitly warns about passing only the entity name, not the question's full noun phrase. This is far more than the schema's terse 'For company: ticker (AAPL), CIK (0000320193), or name.'

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 example queries ('What's the ticker for…' / 'find the CIK for…') and then states the verb-resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It also lists supported types ('company' and 'drug') and what each resolves. This is specific, unambiguous, and clearly distinct from siblings like entity_profile or compare_entities.

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 a strong directive: 'Use FIRST whenever you have a name but need an ID.' It also explains when the tool is applicable across types and warns against passing full noun phrases, giving concrete examples ('NEW YORK ST DORM AUTH' vs. adding 'revenue bonds'). It does not explicitly name alternative tools for other cases, but the 'Use FIRST' guidance plus the detailed scope makes usage clear. A 4 is warranted because it lacks explicit 'when not to use' wording.

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

A4.3/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose. The ask_pipeworx family is differentiated by grounded mode and beta status; prediction-market tools each target a specific analysis (arbitrage, edges, fill risk, cross-venue spread); species tools split search vs. detail vs. occurrences; memory and subscription tools are unambiguous. No two tools appear to do the same thing.

Naming Consistency5/5

Tool names follow consistent snake_case patterns grouped by domain: ask_pipeworx variants, polymarket_* tools, species tools (get_species, search_species, get_occurrences, occurrences_near), memory verbs (remember, recall, forget), subscription verbs (subscribe, unsubscribe, list_subscriptions), and descriptive nouns like entity_profile and deep_research. The style is uniform and predictable.

Tool Count3/5

At 35 tools, the count exceeds the 16-25 range that feels heavy, though it sits below the 50+ extreme. The server is a multi-domain data gateway covering entity research, prediction markets, species, AI visibility, and subscriptions, so the breadth is justified, but the sheer number borders on overwhelming and pushes the score down.

Completeness5/5

The tool surface covers the core CRUD lifecycle for each subdomain: subscriptions have create/list/delete and alert retrieval, memory has save/retrieve/delete, species has search/detail/occurrence lookup, and data queries offer multiple modes (universal, grounded, deep research, claim validation). No obvious dead ends—each workflow has the necessary follow-up tools (e.g., resolve_entity before lookups, search_within for large records).

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