Zooq

companies_employees_data

Read-onlyIdempotent

People who work or worked at an organization (professional records, same shape as /search/people). Cursor-paginated. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description
`slug`	Yes	Company public slug — the part after linkedin.com/company/. Resolve via companies_name_lookup (or /search/companies) if you only have a name — read data[].slug.
`sort`	No	Ordering. Accepted values: newest, oldest, recently_left (use recently_left with current_only=false).
`limit`	No	Results per page, 1-50 (default 20).
`title`	No	Partial job-title filter (min 3 chars), e.g. software engineer. Combine with current_only=true to target a current role.
`cursor`	No	Opaque pagination cursor from the previous response.
`geo_city`	No	City filter (min 3 chars).
`start_year`	No	Match people who started in this year (1900-current).
`start_month`	No	Match people who started in this month (1-12), paired with start_year.
`current_only`	No	Restrict to people in a current role at the company.
`geo_country_code`	No	ISO country code filter, e.g. us.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`items`	No	Array in the example

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, and open-world behavior. The description adds valuable behavioral context beyond annotations: it is cursor-paginated, costs 10 Zooq credits, and returns the same shape as /search/people. 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?

Three short, purposeful sentences. The core purpose is front-loaded, followed by the useful output-shape equivalence, pagination behavior, and cost. No filler or redundancy.

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?

With a 100%-covered schema, annotations, and an output schema present, the description covers the main extra operational details: output shape, pagination mode, and credit cost. It is slightly thin on when to choose this over alternatives, but that gap is already accounted for in usage_guidelines.

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 every parameter is already documented in the input schema. The tool description adds no extra parameter-level meaning, so the baseline of 3 is appropriate.

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 identifies the resource ('people who work or worked at an organization') and adds that they are professional records, distinguishing it from jobs, posts, or company info. It lacks an explicit imperative verb like 'Lists', but the meaning is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is the tool for company-employee/professional records and notes cursor pagination and cost, but it never explicitly says when to prefer it over siblings like search_people, search_alumni, or companies_enrich. No when-not-to-use guidance is provided.

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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