Zooq

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

Name	Required	Description	Default
`email`	Yes	The email address to verify (valid syntax required).

Output Schema

TableJSON Schema

Name	Required	Description
`email`	No	Example value was a string
`reason`	No	Example value was a string
`mx_hosts`	No	Array in the example
`catch_all`	No	Example value was a boolean
`deliverable`	No	Example value was a boolean

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare the tool as read-only and idempotent. The description adds useful framing beyond those annotations by revealing the '+10 Zooq credits' cost and the categories of risk flags returned. No contradiction with annotations appears.

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?

One sentence with no redundant filler. The primary purpose is front-loaded, and the operational cost is appended in a clear parenthetical. The description remains scannable and focused.

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?

The tool is simple: one required parameter, no nested objects, and an output schema exists. The description, annotations, and schema together cover task purpose, credit cost, and input requirements, making the description adequate for 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?

Schema coverage is 100%, so the schema already fully describes the single email parameter, including the valid syntax requirement. The description adds no further parameter-specific detail, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb and resource: checking whether an email address can receive mail, and it names concrete output signals (deliverability verdict, risk flags). It does not explicitly differentiate from sibling email_* tools, but the verification-focused wording makes the tool's role clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly say when to use this tool versus email_find, email_reverse, or other email-related siblings. It provides no alternatives or exclusions, so guidance is left to inference from the tool name and general context.

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

A3.6/5.0

Disambiguation2/5

Many tools have strongly overlapping purposes: companies_name_lookup is explicitly equivalent to search_companies, companies_enrich/companies_info/companies_universal_name_to_id all return company-profile data, and profile_full overlaps with profile_employment_history and profile_enrich. The descriptions are detailed, but an agent would still frequently have to choose between near-duplicate endpoints.

Naming Consistency4/5

Tool names mostly follow a predictable resource-prefixed snake_case pattern, such as companies_*, jobs_*, posts_*, profile_*, and search_*, which makes the set readable and groupable. Minor inconsistencies like jobs_details_v2, g_title_skills_lookup, and mixed noun suffixes (info/details/full/lookup) keep it from a perfect score.

Tool Count2/5

44 tools is well beyond the heavy 25+ band, and several tools appear to be different lookup modes or near-duplicates of the same underlying capability. The broad LinkedIn-style data domain explains much of the size, but the set still feels bloated rather than well-scoped.

Completeness4/5

The API covers the core read-only professional-data workflows well: people, companies, jobs, posts, comments, likes, email discovery/verification, schools, skills, and targeted searches. Minor gaps exist, such as some job filters being unusable and no direct exposure of certain profile alias endpoints, but agents can generally complete end-to-end workflows.

Resources

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