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ZOOQ - LinkedIn Data for AI Agents

email_reverse

Read-onlyIdempotent

Resolve the person and company behind a BUSINESS email address. Public/role/disposable mailboxes are rejected (422, no charge) before any work runs. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesA professional working mailbox. Public providers (gmail/outlook/…), role accounts (info@, support@), disposable and relay addresses are rejected with 422 (no credits charged).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoExample value was a string
foundNoExample value was a boolean
personNo
confidenceNoExample value was a string
current_companyNo

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds meaningful context beyond that: it discloses the 422 rejection behavior for invalid address types (with no charge), and states the cost (10 credits). This gives the agent important operational knowledge about failure modes and pricing, which annotations do not cover.

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?

The description is compact at three sentences, with the primary purpose front-loaded in the first sentence. It efficiently conveys the rejection behavior and cost without redundancy. Every sentence earns its place.

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 single-parameter tool with a well-documented schema, clear annotations, and an output schema present, the description covers what agents need: purpose, input constraints, failure response, and cost. There are no significant missing details that would impede correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'email' is fully described in the schema (100% coverage), and the description's mention of rejection types mirrors that schema text. The description adds no new parameter-specific meaning beyond what the schema already states. Thus the baseline of 3 is appropriate; no extra value provided.

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 'Resolve the person and company behind a BUSINESS email address', a specific verb and resource that clearly states the tool's function. It also distinguishes itself from siblings like email_find and email_verify by emphasizing reverse-lookup (resolving owner from an address) and by restricting to business emails, explicitly excluding public/role/disposable addresses.

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 implies when to use the tool: it is for business emails only, and public/role/disposable addresses are rejected. While it doesn't explicitly name alternative tools, the context of siblings (email_find, email_verify) makes the intended usage clear. A note on using this when you have a professional email and need the associated person/company would be a minor improvement.

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
Disambiguation3/5

Most tools are separated by domain prefixes and the descriptions are unusually explicit about differences, but there are direct overlaps: companies_name_lookup is the same upstream as search_companies, companies_entity_id vs companies_universal_name_to_id resolve different id spaces, and search_people/search_people_live plus search_companies/search_companies_live cover similar ground. An agent can usually pick correctly, but only after close reading.

Naming Consistency4/5

The set is consistently snake_case with readable domain prefixes like companies_, jobs_, posts_, profile_, and search_. Deviations include the unexplained g_* prefix, jobs_details_v2's version suffix, affiliate_program lacking a resource prefix, and the duplicate naming convention of companies_name_lookup vs search_companies.

Tool Count2/5

45 tools is well above the 25+ threshold and creates a heavy surface for an agent to scan. While the domains are broad, some tools are redundant (companies_name_lookup/search_companies) or tangential (affiliate_program), so the count is not fully justified.

Completeness4/5

The server covers people, companies, jobs, posts, email, schools, and skills with both search and detail endpoints, which is strong for a read-only LinkedIn API. Obvious gaps like a global post search or a company followers list are absent, but the existing paths support most workflows without dead ends.