Zooq

companies_enrich

Read-onlyIdempotent

Freshest LIVE company profile. Returns three things the dataset record behind companies_info does not: funding signals, the FULL location list (not just HQ), and parent/affiliated/related pages. Pass slug and Zooq resolves it to the numeric id at no extra credit cost, or pass id from companies_entity_id to skip the lookup. Not-found is free upstream. (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
No arguments

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already cover readOnly, openWorld, idempotent, and destructive behavior. The description adds valuable behavioral context beyond annotations: the profile is 'LIVE', lookup costs 10 Zooq credits, not-found is free upstream, and slug-to-id resolution happens automatically at no extra cost. This is useful operational detail with 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 with the core value proposition, then quickly covers the key parameter and cost details. Every sentence contributes decision-relevant information, with no filler or repetition of schema content.

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 2 optional parameters, a full schema, a rich output schema, and strong annotations, the description covers the essential context: purpose, differentiation from companies_info, parameter routing, cost, and not-found behavior. Nothing necessary for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents both parameters at 100% coverage, so the baseline is 3. The description elevates this by explaining the practical semantics: slug is resolved to numeric id automatically, id can come from companies_entity_id, and both id and slug accept multiple identifier forms. This helps an agent choose between the two parameters.

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 clearly states what the tool does: it returns a fresh LIVE company profile with funding signals, full locations, and parent/affiliated/related pages. It explicitly differentiates from companies_info, a sibling tool, by naming the specific data this tool provides that the dataset record does not.

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?

The description gives actionable selection guidance: use this tool when you need funding, full location lists, or parent/affiliated pages beyond companies_info. It also explains the slug vs id decision and notes that not-found is free, but it does not explicitly state when NOT to use this tool or when to prefer 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.

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