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Serpapi

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?

Even though annotations already mark this as read-only, idempotent, and non-destructive, the description adds substantial behavioral detail: it cascades across multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns `figi_candidates` on ambiguous matches, and labels resolved identifiers by source while listing unresolved ones explicitly. This goes well beyond the 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 front-loaded with purpose and usage, and the supported-types sections are logically organized. It is dense and contains long parenthetical clauses, which makes scanning harder, but the verbosity is largely justified by the tool's multi-source, multi-type complexity.

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 covers what an agent needs: input formats, output identifier types, source provenance, ambiguous-match behavior, unresolved-identifier handling, and fallback degradation. It is sufficiently complete for both selection and correct invocation.

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 already 100%, but the description adds important operational meaning: what forms `value` may take for each type, that a bond should be passed as the issuer name only, and that trailing security-class words will fail FIGI lookup. This meaningfully goes beyond the schema's property 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 names a specific action—resolving user-spoken names to canonical/official identifiers—and gives concrete examples ('ticker for', 'CIK for', 'LEI for', 'RxCUI for'). It also positions the tool as the first stop when a name is known but an ID is needed, which separates it from other lookup-style siblings.

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?

The description gives strong when-to-use guidance ('Use FIRST whenever you have a name but need an ID') and concrete usage examples for company and drug lookups. It does not explicitly name alternatives or state when not to use this tool, so it stops short of full exclusion guidance.

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

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research all querying data but with nuanced differences. The descriptions help but boundaries remain fuzzy, especially between ask_pipeworx and deep_research for broad vs. single lookups. Overall moderate ambiguity.

Naming Consistency2/5

Naming is inconsistent: some tools use snake_case (ask_pipeworx, ai_visibility_check), others use camelCase (serpapi_google_jobs), and patterns vary widely (e.g., pipeworx_feedback vs. compare_entities). Only the serpapi_google_* group follows a consistent pattern.

Tool Count3/5

36 tools is on the high side for a single server, with many meta-tools (discover_tools, suggest_questions) and niche prediction market tools. The scope seems overly broad, covering data lookup, prediction markets, memory, and subscriptions, which could be streamlined to a more focused set.

Completeness3/5

The server covers a wide range of domains (financial, economic, news, drugs, prediction markets, Google services), but lacks direct web search and write/update capabilities. While the coverage is broad, there are notable gaps (e.g., no generic web search, limited tool for modifying data) for a data-focused server.

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