Zooq

email_find

Read-onlyIdempotent

Discover a person's work email from their first name, last name, and company domain. Returns the address plus a confidence score. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description
`domain`	Yes	Company domain as a bare hostname — no scheme, no @, must contain a dot.
`last_name`	Yes	The person's last name.
`first_name`	Yes	The person's first name.

Output Schema

TableJSON Schema

Name	Required	Description
`email`	No	Example value was a string
`found`	No	Example value was a boolean
`catch_all`	No	Example value was a boolean
`confidence`	No	Example value was a string

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable extra context outside those annotations: the 10-Zooq credit cost and the fact that it returns a confidence score. No contradictions 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.

Conciseness5/5

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

Two sentences with no wasted content. The first sentence states the purpose and inputs, and the second adds the return value and cost. Requirements are front-loaded and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the purpose, inputs, output format, and cost, and the annotations plus output schema cover safety and result shape. It lacks only an explicit pointer to sibling tools, but for a tool of this simplicity, precious required knowledge is not missing.

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 description coverage is 100%, so the input schema already documents all three parameters, including the format guidance for 'domain'. The description only restates the parameter names without adding details. A baseline 3 is appropriate when the schema carries the semantic weight.

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 tool finds a person's work email using first name, last name, and company domain, and that it returns the address plus a confidence score. It is specific and unambiguous, though it does not explicitly differentiate itself from siblings like email_find_by_profile 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 implies the intended use case: when you have a person's name and company domain, this tool resolves the email. It names the required inputs, providing clear context for when to call it, but it does not explicitly mention alternatives or when not to use it.

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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