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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

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses important behaviors: graceful degradation when GLEIF or OpenFIGI is unavailable, cascading internal lookups, explicit unresolved identifier reporting, and the ability to resolve non-equity instruments. This is rich behavioral context that annotations alone do not provide.

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 organized and front-loaded with the core purpose and example queries. Nearly every sentence adds necessary detail for a complex multi-source resolver; it could be slightly tightened, but the density is justified given the tool's scope.

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 and the tool's complexity, the description thoroughly covers return contents (RxCUI, ingredient, brand, CIK, ticker, LEI, FIGI), failure modes (graceful degradation, unresolved list), and input edge cases. An agent has enough context to invoke 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: it clarifies the 'value' parameter with entity-name-only instruction, warns against including trailing security-class words, provides examples for both company and drug types, and explains the ISIN-to-LEI mapping. This goes well beyond the schema's basic 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 specific verb ('resolve') and resource (user-spoken names to canonical identifiers), and reinforces it with concrete example queries ('what's the ticker...', 'find the CIK...'). It clearly distinguishes this from sibling tools by framing it as the first step when a name but not an ID is available.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs 'Use FIRST whenever you have a name but need an ID,' giving clear when-to-use guidance. It also explains how the tool replaces 2-3 manual lookups and how it handles different input forms, making the decision boundary concrete.

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

Several tools overlap in purpose (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim all handle factual queries), which could confuse an agent. However, detailed descriptions help differentiate them, so the confusion is moderate.

Naming Consistency4/5

Most tools follow a consistent snake_case verb_noun pattern (e.g., resolve_entity, compare_entities). A few irregular verbs (forget, recall, remember) and diverse prefixes (pipeworx_, polymarket_) lower consistency slightly.

Tool Count3/5

33 tools is high but each serves a distinct purpose within a broad domain (data querying, prediction markets, security, memory, etc.). The number feels slightly excessive for a single server, but not extreme.

Completeness4/5

The tool surface covers many aspects of data retrieval, prediction market analysis, and security checks. Minor gaps exist (e.g., no dedicated WHOIS or CVE lookup), but core workflows are well-supported.

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