Zooq

email_find_by_profile

Read-onlyIdempotent

Identify a person and their current company from a professional profile URL (or handle), then find their work email — resolves name + domain for you. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes	A professional profile URL or its bare public handle.

Output Schema

TableJSON Schema

Name	Required	Description
`url`	No	Example value was a string
`email`	No	Example value was a string
`found`	No	Example value was a boolean
`domain`	No	Example value was a string
`company`	No	Example value was a string
`catch_all`	No	Example value was a boolean
`last_name`	No	Example value was a string
`confidence`	No	Example value was a string
`first_name`	No	Example value was a string

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already indicate read-only, idempotent, open-world, and non-destructive behavior. The description adds useful behavioral context beyond those hints: it performs a two-step resolution of name and domain, and it costs 10 Zooq credits. This is valuable additional information for a calling agent.

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 concise sentences with no filler. The core behavior is front-loaded, and the credit cost is appended as an important but secondary detail. 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?

Given the tool's single parameter, rich annotations, and an existing output schema, the description covers the essential operational facts: acceptable input forms, what the tool resolves, and the cost. Nothing critical is missing for an agent to invoke it 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 input schema already covers 100% of the parameter, including the fact that 'url' is a professional profile URL or bare public handle. The description restates this concept but does not add new parameter-level semantics, so the schema-documented baseline of 3 is appropriate.

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 and resource: identify a person and company from a professional profile URL or handle, then find their work email. This clearly differentiates it from other email sibling tools like email_find, email_reverse, and email_verify, all of which operate from different inputs.

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 makes the input condition clear: use this when you have a professional profile URL or bare public handle. It does not explicitly say when not to use it or name an alternative, but the context is strong enough that an agent can infer the intended use case.

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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