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ZOOQ - LinkedIn Data for AI Agents

by io.github.baldiga

companies_affiliated_pages

Read-onlyIdempotent

Affiliated / subsidiary / showcase pages of a company. Keyed by the numeric organization id: pass slug and Zooq resolves it for you at no extra credit cost, or pass id from companies_entity_id to skip the lookup. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoNumeric 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`.
slugNoCompany 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
NameRequiredDescriptionDefault
countNoExample value was a number
affiliatedPagesNoArray in the example

TDQS

A4.2/5.0
Behavior4/5

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

The description discloses the 10-Zooq-credit cost and explains that slug resolution is included at no extra credit cost, or that a numeric id skips the lookup. Annotations already cover read-only/idempotent behavior, so the description adds useful non-obvious details beyond them.

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?

Three short sentences front-load the resource and keying mechanism, then add cost information. No wasted words or redundant schema repetition.

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?

For a two-parameter, read-only tool with a full output schema and annotations covering safety hints, the description covers purpose, identifier resolution, and cost. There are no critical gaps for an agent to call it correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning by referencing `companies_entity_id` as the source of `id` and by noting slug resolution costs no extra credit. This clarifies how the two optional params relate.

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 opening phrase names the exact resource ('Affiliated / subsidiary / showcase pages of a company'), and the annotation title supplies the verb 'List'. It is clearly scoped to company page relationships, though it does not contrast with sibling tools.

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?

It gives clear selection context: use the tool when you need affiliated/subsidiary/showcase pages for a company, and choose `slug` for autolookup or `id` from companies_entity_id to skip lookup. It does not enumerate alternatives or exclusions, but the context is unambiguous.

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.

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