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Bnr Ro

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.

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already signal read-only, idempotent, and non-destructive behavior, and the description adds substantial behavioral context beyond those hints: 'Each call cascades through several lookup endpoints internally,' LEI/FIGI enrichment 'degrades gracefully,' multi-match cases 'assert nothing and return figi_candidates,' and unresolved identifiers are 'stated explicitly under unresolved rather than omitted.' 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 long but well organized into recognizable sections and front-loaded with concrete example queries. Every block adds relevant information about supported types, input formats, edge cases, and failure behavior; the main cost is density, not redundancy, so it earns a strong score but not a perfect one.

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's complexity and the absence of an output schema, the description is remarkably complete. It explains return contents (CIK, ticker, company_name, LEI, FIGI, RxCUI, ingredient, brand), labels source provenance, covers the `figi_candidates` and `unresolved` cases, and clarifies graceful degradation. An agent has enough context to invoke the tool correctly across company and drug queries.

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 schema description coverage is 100%, the description deeply enriches both parameters. It explains what value means for company versus drug, gives concrete examples (AAPL, 0000320193, ozempic), and warns against passing full noun phrases for bonds, noting the FIGI lookup matches instrument names. This goes well beyond the schema's minimal descriptions.

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 precise verb and resource: it 'resolve[s] a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates concrete query forms (ticker, CIK, LEI, RxCUI) and explicitly distinguishes its job from other tools by saying 'Use FIRST whenever you have a name but need an ID.' This clearly differentiates it from siblings like entity_profile and 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 explicit when-to-use guidance ('Use FIRST whenever you have a name but need an ID') and details what input forms each type accepts. It does not explicitly name alternatives or state when not to use this tool, so it stops short of the fullest exclusionary guidance, but the usage context is clear.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim, which could cause confusion. However, the descriptions help clarify when to use each.

Naming Consistency3/5

Tool names use a mix of patterns (verb_noun, noun_noun, adjective_noun) but are consistently lowercase with underscores. Some names are vague like 'forever' and 'recent_alerts', but overall readable.

Tool Count3/5

32 tools is on the high side for a single server, but many are specialized and serve a broad data query platform. Some tools are meta-tools covering multiple use cases, which could reduce the need for so many.

Completeness4/5

The tool set covers a wide range of functionalities including data querying, entity resolution, comparisons, verification, memory, subscriptions, and prediction markets. Minor gaps like data export are not critical for its purpose.

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