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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

email_prospects

Read-onlyIdempotent

Page emails already known for a company domain. Cursor-paginated; returns up to 20 contacts per page with first/last name. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesWhich addresses to return. Accepted values: full (all known emails), verified_only (deliverable only).
cursorNoOpaque pagination cursor. Omit for the first page; pass the previous response's next_cursor for the next.
domainYesCompany domain as a bare hostname (no scheme, no @).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoExample value was a string
countNoExample value was a number
domainNoExample value was a string
prospectsNoArray in the example
next_cursorNoExample value was a string

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond annotations: the 10-credit cost, the 20-result page limit, cursor-based pagination, and the returned first/last name fields.

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 compact sentences contain all essential operational details with no filler. Scope, pagination behavior, output shape, and cost are all front-loaded and clearly presented.

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?

With rich annotations, a complete input schema, and an output schema present, the description covers the remaining operational essentials: credit cost, page size, and pagination. Nothing critical is missing 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 description coverage is 100%, so the schema fully documents domain, kind, and cursor. The description does not add new parameter semantics beyond the mention of pagination, which is appropriate given the schema already handles the details.

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 ('Page') and a specific resource ('emails already known for a company domain'). The phrase 'already known' distinguishes it from discovery-oriented siblings like email_find, and the pagination detail clarifies the operation.

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 frames when to use the tool: when you need existing emails for a domain and want to page through them. It does not explicitly exclude alternatives, but the context is specific enough to guide selection.

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.

Resources