Zooq

companies_insights

Read-onlyIdempotent

Employee-count total + distribution buckets (by department, seniority, location). Keyed by the numeric organization id: pass slug and Zooq resolves it for you at no extra credit cost, or pass id from companies_entity_id to skip the lookup. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	No	Numeric organization id from companies_entity_id; the urn:li:organization: form is accepted. An org_ id (dataset namespace, from companies_info) or a slug placed here is recognized and translated automatically. Provide `id` OR `slug`.
`slug`	No	Company public slug — the part after linkedin.com/company/ — or the full company URL. Resolved to `id` automatically at no extra credit cost. Any company identifier is accepted here and sorted by format (slug, URL, numeric id, org_ id). Provide `id` OR `slug`; `slug` is the simplest.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`groups`	No	Array in the example
`totalResultCount`	No	Example value was a number

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context beyond annotations by disclosing the 10-Zooq-credit cost and noting that slug resolution costs no extra credits, which an agent needs to know before invoking.

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 dense sentences deliver the output summary, identifier guidance, and cost information without redundancy. The most important information is front-loaded, and every clause earns its place.

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?

The description covers what data is returned, how to pass identifiers, and the credit cost, while the output schema handles return-value details. It is complete for a read-only, two-parameter tool, though explicit routing to sibling tools would make it even stronger.

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 already documents both id and slug in detail. The description adds the useful cost distinction between the two identifier paths, but otherwise does not contribute significant new parameter semantics beyond what the schema provides.

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 states the tool returns an employee-count total plus distribution buckets by department, seniority, and location. This is specific enough to distinguish it from related sibling tools like companies_employees_data (raw employee data) or companies_info (general company info), though it does not explicitly name them.

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 explains the two identifier options (slug vs. id) and the credit cost, which is useful operational guidance. However, it does not explicitly state when to prefer this tool over alternatives such as companies_employees_data or companies_enrich, so the use case is only implied by the output summary.

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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