Zooq

companies_similar

Read-onlyIdempotent

Similar companies / peers (id, name, industry, followers, url). 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

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
`count`	No	Example value was a number
`SmilarCompanies`	No	Array in the example

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds value beyond annotations by disclosing the 10-credit cost, that slug resolution happens automatically at no extra cost, and that using the id avoids the lookup step. This gives an agent useful expectations not present in the structured 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?

Two concise sentences front-load the tool's purpose and output fields, then explain the keying mechanism and cost. There is no filler, and the structure is easy to scan.

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 read-only lookup tool with rich annotations and a complete input schema, the description covers purpose, output fields, identifier handling, and cost. Nothing essential is missing for an agent to select and invoke this tool 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?

The schema already documents both parameters thoroughly, giving 100% schema description coverage. The description still adds meaning by highlighting the cost difference between passing a slug versus an id, and pointing to companies_entity_id as the source for the id, which helps an agent pick between the two optional 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 opens with 'Similar companies / peers' and lists the output fields (id, name, industry, followers, url), making the tool's purpose immediately specific. This clearly distinguishes it from sibling tools like companies_info or companies_insights, which address different company-related needs.

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?

While it does not name explicit alternatives, it gives clear operational context: use this tool to retrieve similar companies, passing either a slug or an id from companies_entity_id. The id/slug guidance and credit-cost note help an agent decide how to invoke it, though it does not spell out when to prefer a sibling tool.

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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