ZOOQ - LinkedIn Data for AI Agents

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

A3.8/5.0

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 a genuinely useful cost warning ('Costs 10 Zooq credits'), which is a behavioral constraint beyond the annotations. No contradiction with the declared hints.

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?

A single, front-loaded sentence states the core value, lists the key fields, and adds the cost caveat as a parenthetical. Every element earns its place with no 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?

For a simple read-only lookup with complete input schema, output schema, and safety annotations, the description is nearly sufficient. It includes the cost warning and enough field detail to orient an agent, though explicit routing against sibling tools would make it fully complete.

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%, with both id and slug documented and the 'Provide id OR slug' constraint already present. The description itself adds no additional parameter meaning, 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 title 'Get company profile' supplies the verb, and the description specifies the exact resource scope with concrete fields: description, industry, headcount, HQ, follower count, specialties. It does not explicitly distinguish itself from siblings like companies_enrich or companies_insights, but the firmographics field list makes the purpose clear.

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 core company firmographic data, but it does not explicitly state when to prefer it over alternatives such as companies_enrich, companies_insights, or companies_entity_id. There is no when-not-to-use guidance or naming of sibling alternatives.

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.

companies_info

Input Schema

Output Schema

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