Zooq

companies_info

Read-onlyIdempotent

Full company firmographics — description, industry, headcount, HQ, follower count, specialties. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	No	Stable company id (org_...). Get it from companies_info or /search/companies — read data.id. Provide id OR slug.
`slug`	No	Company public slug (after linkedin.com/company/). Provide id OR slug.

Output Schema

TableJSON Schema

Name	Required	Description
`id`	No	Example value was a string
`url`	No	Example value was a string
`name`	No	Example value was a string
`slug`	No	Example value was a string
`type`	No	Example value was a string
`hq_city`	No	Example value was a string
`website`	No	Example value was a string
`logo_url`	No	Example value was a string
`headcount`	No	Example value was a number
`industries`	No	Array in the example
`description`	No	Example value was a string
`headquarter`	No
`specialities`	No	Array in the example
`industries_v2`	No	Array in the example
`follower_count`	No	Example value was a number
`headcount_range`	No	Example value was a string
`hq_country_code`	No	Example value was a string

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond annotations: the 10 Zooq credit cost, which affects agent planning. It does not disclose rate limits or pagination, but with annotations covering the safety profile, the added cost disclosure justifies a strong score.

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 a single compact clause front-loaded with the core purpose ('Full company firmographics') followed immediately by the most important caveat (cost). There is no filler; every element 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?

With a full output schema, 100% schema coverage on both parameters, and annotations clarifying safety, the description plus structured metadata is sufficient for an agent to select and invoke the tool. It could have explicitly stated the id-or-slug requirement, but that is already captured in the parameter descriptions, so the gap is minor.

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 parameter documentation already explains 'id' and 'slug' including the 'Provide id OR slug' constraint. The tool description itself adds no extra parameter-level detail, so the baseline of 3 applies.

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 and the kind of data returned: 'Full company firmographics — description, industry, headcount, HQ, follower count, specialties.' This tells an agent exactly what the tool exposes. It does not explicitly contrast with similar siblings like companies_enrich, so it stops short of full differentiation.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives such as companies_enrich, companies_entity_id, or companies_name_lookup. The only extra signal is the credit cost, which is a constraint rather than a usage criterion. The description neither states when to prefer this tool nor mentions exclusions.

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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