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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.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses important behaviors: graceful degradation when GLEIF/OpenFIGI fail, ambiguous matches returning figi_candidates instead of asserting, unresolved identifiers explicitly stated, and multi-source cascading. This is rich, honest behavioral context.

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 section carries operational value: examples, supported types, failure behavior, and input pitfalls. It is front-loaded with the trigger phrase, though a wall of unbroken text with many embedded clauses could be better structured for scanning.

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 compensates by explaining result behavior: candidate lists on ambiguity, an unresolved section, source labels, and the drug result composition. It also covers input variants and edge cases such as non-US issuers and non-equity instruments. An agent has enough to call and interpret it correctly.

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 coverage is 100%, the description materially enriches the parameters: it gives concrete value formats (AAPL, 0000320193), warns to pass the entity name only, uses a bond issuer example, and explains why trailing security-class words fail. This is far beyond schema-level documentation.

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 uses a specific verb ('resolve') against a well-defined resource: user-spoken names to official identifiers. It enumerates concrete example queries and supported entity types, so an agent immediately understands the tool's role and how it differs from generic lookup tools.

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 explains it replaces 2-3 manual lookups and covers when to use each type. However, it does not name sibling alternatives or state when not to use this tool versus tools like entity_profile or compare_entities.

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
Disambiguation3/5

There is notable overlap between tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, as well as between polymarket_edges and bet_research. While descriptions differentiate them, an agent may struggle to pick the right one without deep understanding of nuances.

Naming Consistency3/5

Tool names use mixed conventions: snake_case (ai_visibility_check), verb_noun (ask_pipeworx, bet_research), and single-word verbs (forget, recall). Some names are very long (polymarket_fill_risk) while others are terse, reducing predictability.

Tool Count3/5

33 tools is high but can be justified by the broad domain coverage (data retrieval, entity resolution, comparisons, memory, subscriptions). However, several tools serve similar purposes, suggesting potential consolidation.

Completeness4/5

The tool set covers a wide range of data sources and tasks (SEC, FDA, real estate, prediction markets, etc.) with CRUD-like operations on memory and subscriptions. Minor gaps exist, such as no direct stock trading or social media monitoring, but overall coverage is strong.

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