Zooq

companies_entity_id

Read-onlyIdempotent

Resolve a company slug to the numeric organization id used by the live company endpoints (posts, similar, affiliated, insights). Resolve once, reuse the id. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes	Company public slug — the part after linkedin.com/company/. A full company URL works too.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	No	Example value was a number
`slug`	No	Example value was a string

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already cover safety by flagging readOnlyHint, idempotentHint, and non-destructiveness. The description adds a cost-related behavioral detail (10 Zooq credits) and a reuse strategy, which is valuable context beyond the structured annotations. No contradiction.

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 compact and front-loaded: purpose first, followed by usage advice and credit-cost caveat. Every clause has a purpose, and there is no filler or repetition of annotation values.

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 one-parameter tool with an output schema present, the description covers purpose, target endpoints, cost, and reuse guidance. It could briefly distinguish itself from sibling name-to-id tools, but that is a minor omission and does not materially hinder correct invocation.

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 single parameter 'slug' is already fully described in the input schema, including the format and acceptance of full URLs. The tool description adds no extra parameter semantics beyond the slug-relation concept, so schema coverage is doing the heavy lifting.

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?

States a specific verb ('resolve'), a resource ('company slug'), and the output ('numeric organization id'). The description also ties the id to the live company endpoints, distinguishing it from generic id-lookup siblings without needing their schemas.

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

Usage Guidelines4/5

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

Provides clear usage context: use this when you need the numeric id for live company endpoints, and resolve once then reuse to avoid repeated cost. It does not explicitly name alternatives like companies_universal_name_to_id or spell out when not to use this tool, so it stops short of a 5.

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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