Zooq

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

Name	Required	Description	Default
`email`	Yes	A 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

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

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds real behavioral context: invalid mailboxes are rejected with 422, no charge is incurred, no work runs on them, and a successful lookup costs 10 Zooq credits. This goes beyond what the annotations alone state.

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 three short, information-dense sentences, front-loaded with the core purpose and followed by rejection behavior and cost. Every sentence contributes useful guidance with no redundant filler.

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 read-only tool with a rich schema description and an output schema, the description is complete: purpose, input constraints, failure behavior, cost, and safety profile are all covered. No critical detail is 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 coverage is 100%, so the schema fully documents the email parameter, including rejection criteria. The description echoes some of that context but does not add much beyond the schema's own value; baseline 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 states a clear action and object: 'Resolve the person and company behind a BUSINESS email address.' This is a specific reverse-lookup purpose that distinguishes it from sibling tools like email_find or email_verify, which solve different problems.

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 clearly establishes when to use the tool: with a business email to resolve person/company. It also gives explicit exclusions by stating public, role, disposable, and relay mailboxes are rejected with 422. It does not explicitly name alternate tools, so it stops short of full routing guidance.

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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