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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

email_verify

Read-onlyIdempotent

Check whether an email address can receive mail, with a deliverability verdict and risk flags (catch-all, disposable, no-MX). (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesThe email address to verify (valid syntax required).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoExample value was a string
reasonNoExample value was a string
mx_hostsNoArray in the example
catch_allNoExample value was a boolean
deliverableNoExample value was a boolean

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by disclosing the 10-credit cost and specifying the risk flags returned, which helps the agent anticipate cost and result semantics. No contradiction with the annotations exists.

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 one information-dense sentence with the cost in an unobtrusive parenthetical and no filler. The most important fact, that this verifies deliverability, is front-loaded.

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 a single documented parameter and an output schema available, the description covers all additional context an agent needs: the cost, the verdict, and the risk flags. There is no material gap for calling this tool correctly.

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 single parameter is fully described in the schema with 'valid syntax required,' so the description does not need to repeat it. The description adds no additional parameter-level semantics, matching the baseline for 100% schema coverage.

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 a specific verb-resource pair ('Check whether an email address can receive mail') and specifies the result categories: deliverability verdict and risk flags for catch-all, disposable, and no-MX. This clearly distinguishes it from sibling email tools like email_find or email_reverse.

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 conveys a clear use case: verifying deliverability before sending mail, and it conspicuously notes the 10-credit cost. It does not explicitly name alternatives or exclusion conditions, so it falls just short of a 5.

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.

Resources