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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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, so the bar is lower, but the description richly adds beyond them: cascades through multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, returns figi_candidates on ambiguity, and explicitly reports unresolved identifiers. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Long but densely informative; every section earns its place: example utterances, the core directive, supported types, input caveats, and degradation behavior. Front-loaded with the 'resolve a name to an ID' purpose and 'Use FIRST' guidance. For a two-type, multi-identifier resolver, this is appropriate compression.

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?

Despite no output schema, the description explains what identifiers are returned (CIK, ticker, company_name, LEI with ownership, FIGI, RxCUI, ingredient, brand), how ambiguity is reported via figi_candidates, and how degraded availability is handled. An agent has enough to call the tool and interpret results 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?

Schema coverage is 100%, but the description adds substantial meaning beyond the schema: concrete ticker/CIK/name examples, drug brand/generic examples, the 'entity name only' rule for bonds, and the warning that trailing security-class words will fail FIGI matching. This goes far beyond the schema's minimal field 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?

States a specific verb+resource: resolve a user-spoken NAME to canonical/official identifiers. Explicitly distinguishes from siblings by noting these are identifiers other tools require as input and that it should be used first when a name needs an ID. Supported types and identifier targets are fully listed.

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?

Explicitly says 'Use FIRST whenever you have a name but need an ID' and gives supported types, accepted inputs, and coverage details. It provides clear context but does not name specific sibling alternatives or state when not to use it beyond the general name-to-ID trigger.

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 clearly distinct purposes, but there is some overlap among the meta-querying tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools, which could cause confusion for an agent deciding which to use.

Naming Consistency3/5

Tool names use a mix of verb_noun and noun patterns, with snake_case throughout but no single consistent structure (e.g., ask_pipeworx vs. bet_research vs. dataset). The naming is readable but not uniform.

Tool Count4/5

At 33 tools, the count is on the higher side but justifiable given the broad scope of the Pipeworx platform, covering data querying, entity analysis, prediction markets, memory, subscriptions, and feedback.

Completeness4/5

The toolset covers a wide range of data sources and operations, including querying, entity profiling, comparisons, prediction market analysis, and monitoring. Minor gaps exist (e.g., no direct SEC filing viewer), but the meta-tools handle these adequately.

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