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ZOOQ - LinkedIn Data for AI Agents

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
NameRequiredDescriptionDefault
urlYesA professional profile URL or its bare public handle.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoExample value was a string
emailNoExample value was a string
foundNoExample value was a boolean
domainNoExample value was a string
companyNoExample value was a string
catch_allNoExample value was a boolean
last_nameNoExample value was a string
confidenceNoExample value was a string
first_nameNoExample 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing the 10-credit cost and by stating that it 'resolves name + domain for you,' which sets expectations for enrichment behavior. No contradiction 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 filler: the first states the core function and input, the second discloses the cost. The main behavior is front-loaded, and 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?

For a read-only, idempotent lookup with a single parameter and an output schema, the description covers all invocation-critical details: input form, resolution behavior, and cost. The output schema handles return-value documentation, so nothing an agent needs to call it correctly 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%; the parameter description 'A professional profile URL or its bare public handle' fully defines the accepted input forms. The main description repeats this without adding new syntactic or format details. Baseline 3 is appropriate because the schema carries the semantic weight.

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 specific verb and resource: 'Identify a person and their current company from a professional profile URL (or handle), then find their work email.' This clearly distinguishes it from siblings like email_find (likely input is email) and email_verify (verify existing email) by specifying the input is a profile URL or handle. The title 'Find email from LinkedIn profile' reinforces the purpose.

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 intended usage is explicit: provide a professional profile URL or bare handle, and the tool resolves name + domain then finds work email. There is no explicit 'when not to use' or alternative routing, but the input condition is so clearly stated that an agent can infer when this is the appropriate tool among siblings.

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.