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Attom

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 establish read-only, idempotent, non-destructive behavior. The description adds substantial detail beyond annotations: cross-source resolution via EDGAR, GLEIF, and OpenFIGI; graceful degradation when enrichment source is unavailable; explicit `unresolved` output; `figi_candidates` ambiguity handling; and internal endpoint cascading. 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.

Conciseness4/5

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

The description is strongly front-loaded and information-dense, with the use-first guidance up front. However, the supported-types section is a long near run-on block with deeply nested parentheticals, making it harder to parse. Every sentence earns its place, but the structure could be clearer.

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?

For a tool with no output schema, the description carries the full burden and succeeds: it covers input variants, supported entity types, edge cases like ambiguous bond issuers, unresolved identifiers, enrichment failure degradation, and why this tool replaces multiple manual lookups. An agent has enough context to decide when and how to call it.

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 meaning well beyond it: value must be the entity name only, not a noun phrase; bond issuers should be passed exactly as printed with a concrete warning about trailing security-class words; it gives ticker/CIK/ISIN examples for company and brand/generic examples for drug. This materially improves correct invocation.

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 clearly states the tool resolves a user-spoken name into canonical/official identifiers that other tools require as input, using verbs like 'resolve' and 'look up.' It lists concrete example queries and distinguishes itself from likely siblings such as entity_profile by focusing on identifier resolution across company and drug types.

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 directive: 'Use FIRST whenever you have a name but need an ID,' and enumerates the two supported types with examples. It stops short of naming sibling alternatives or giving explicit when-not-to-use guidance, but the context for use is crisp and immediately actionable.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and ask_pipeworx_grounded shares the same router. The universal ask_pipeworx router also subsumes many domain-specific tools (attom_*, entity_profile, etc.), making it unclear when to use the specialist tools versus the catch-all.

Naming Consistency3/5

All names are snake_case and mostly descriptive, but conventions vary: ask_* and attom_* prefixes coexist with bare verbs (remember, forget, subscribe), noun phrases (entity_profile, polymarket_edges), and adjective-prefixed names (recent_alerts, recent_changes). The pattern is readable but not uniform.

Tool Count2/5

39 tools is well over the 25+ threshold for a heavy surface, especially for a server named 'Attom' that also includes memory, subscriptions, feedback, npm scanning, and AI-visibility tools beyond real estate. Many tools could be consolidated (e.g., the three ask_pipeworx variants, the six polymarket tools).

Completeness4/5

The real estate domain is well covered (search, detail, AVM, rental AVM, sales history, trends, assessment, schools), and the broader data platform includes discovery, grounded answers, entity profiles, comparisons, claim validation, subscriptions, and memory. Minor gaps exist only around edge features like OAuth-gated subscription persistence.

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