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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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint:false, and the description adds substantial non-obvious behavior: cascading internal lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit unresolved identifiers, figi_candidates on ambiguous matches, and ISIN-to-LEI mapping. This is far beyond the annotation coverage and is fully consistent with it.

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-structured, front-loaded with example queries and the primary directive. The company-type parenthetical is dense and could be tightened, but nearly every detail earns its place given the tool's complexity and the absence of an output schema.

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 carries the full burden of explaining return behavior, and it does so thoroughly: source-labeled identifiers, unresolved lists, ambiguous candidate selection, graceful degradation, and input constraints. For a two-parameter, high-complexity resolver, there is no essential missing context.

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%, so the baseline is 3, but the description adds valuable semantics: it maps the type enum to concrete identifier outputs, documents accepted value forms (ticker/CIK/name; brand/generic), and gives a critical caveat about passing the issuer name only rather than a full bond noun phrase. This substantially exceeds what the schema alone communicates.

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 natural-language examples and explicitly states the verb+resource: resolve a user-spoken NAME to canonical/official identifiers other tools require as input. It goes on to enumerate supported types and outputs (CIK, ticker, LEI, FIGI, RxCUI), making it clearly distinguishable from sibling tools like entity_profile or compare_entities as an identity-lookup tool.

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 provides an explicit trigger condition: 'Use FIRST whenever you have a name but need an ID.' This is clear usage guidance, but it does not name alternative tools or explicitly state when not to use this tool, so it stops short of being a full when/when-not/alternatives specification.

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

A4/5.0
Disambiguation3/5

Several tools overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as query routers, with the beta variant currently identical to the stable one. However, most other tools have clearly distinct purposes (memory, subscriptions, prediction market analytics), and the detailed descriptions help differentiate them.

Naming Consistency4/5

All tool names use snake_case and are descriptive, with consistent domain prefixes like pipeworx_ for meta tools and polymarket_ for prediction markets. Some names mix noun-phrase and verb-noun patterns (e.g., ai_visibility_check vs. resolve_entity), but the overall style is predictable and readable.

Tool Count2/5

With 31 tools, the server exceeds the 25-tool threshold for a coherent set. While the broad scope (data querying, prediction markets, memory, subscriptions, AI visibility) justifies many tools, the sheer number creates cognitive load and makes selection harder for agents.

Completeness4/5

The tool surface is quite comprehensive for its domains: querying has ask_pipeworx, grounded answer, deep research, entity profiles, comparisons, and claim validation; prediction markets have research, arbitrage, edge tracking, and fill risk; memory and subscription lifecycles are covered. Minor gaps exist (e.g., no subscription update) but are workable.

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