Zooq

companies_universal_name_to_id

Read-onlyIdempotent

Resolve a company slug (the part after linkedin.com/company/) to its stable org_ id — the dataset id used by /companies/info. For the live company endpoints (posts, similar, affiliated, insights) use companies_entity_id instead; the two ids are not interchangeable. Returns the FULL company record (identical to /companies/info) — read data.id. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes	Company public slug — the part after linkedin.com/company/.

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

A4.5/5.0

Behavior4/5

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

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds useful behavior beyond that: it returns the full company record identical to /companies/info, tells the agent to read data.id, and discloses the 10-credit cost. This is meaningful extra context and does not contradict the 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?

The description is three focused sentences: the core purpose, the critical sibling distinction, and the return format plus cost. Every sentence earns its place and there is no filler.

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?

Given the tool has only one parameter and an output schema, the description covers everything needed to call it correctly: what the input is, what the output contains, the cost, and the main alternative. Nothing essential is missing.

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?

The schema already documents the single slug parameter with 100% coverage, including the same 'part after linkedin.com/company/' definition. The description repeats that context but does not add new parameter-level format, validation, or edge-case information, so it stays at the baseline.

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 transformation — resolve a company slug to its stable org_ id — and ties that id to /companies/info. It also explicitly distinguishes the tool from companies_entity_id, making it easy to tell apart from the closest sibling.

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

Usage Guidelines5/5

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

The description gives an explicit when-to-use versus when-not-to-use rule: for live company endpoints use companies_entity_id instead, because the two ids are not interchangeable. It also implies the correct context, needing the stable dataset id for /companies/info.

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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