ZOOQ - LinkedIn Data for AI Agents

search_people

Read-onlyIdempotent

Search professional records with rich filters — name, title, company, skills, education, tenure, geography. Cursor-paginated. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Results per page, 1-50 (default 20).
`title`	No	Job-title match (min 3 chars).
`cursor`	No	Opaque pagination cursor; omit for the first page.
`degree`	No	Degree filter.
`skills`	No	Comma-separated normalized skill names. Resolve via /g/title-skills-lookup.
`summary`	No	Free-text summary/about match (min 3 chars).
`geo_city`	No	City name (min 3 chars).
`headline`	No	Free-text headline match (min 3 chars).
`last_name`	No	Last name (min 3 chars).
`first_name`	No	First name (min 3 chars).
`is_creator`	No	Only content creators.
`is_premium`	No	Only premium members.
`current_only`	No	Restrict title/company matches to current positions.
`is_boomerang`	No	Only people who rejoined a former employer.
`skills_match`	No	Skill match mode. Accepted values: any (default), all.
`certifications`	No	Certification name filter.
`field_of_study`	No	Field-of-study filter.
`education_level`	No	Education level filter.
`institution_ids`	No	Comma-separated institution ids (inst_...). Resolve via /search/schools.
`skill_count_max`	No	Maximum number of listed skills.
`skill_count_min`	No	Minimum number of listed skills.
`speaks_language`	No	Spoken-language filter.
`geo_country_code`	No	ISO country code.
`last_change_type`	No	Job-change type. Accepted values: joined, left, title_change.
`primary_language`	No	Profile primary language code, e.g. en.
`tenure_max_years`	No	Maximum tenure in current role (years).
`tenure_min_years`	No	Minimum tenure in current role (years).
`company_count_max`	No	Maximum number of companies in history.
`company_count_min`	No	Minimum number of companies in history.
`organization_slugs`	No	Comma-separated company slugs — the part after linkedin.com/company/. Company URLs and org_ ids (from companies_info / search_companies) are accepted and translated. Combine with current_only=true for people who work there NOW.
`certification_authority`	No	Certification issuing authority filter.
`last_change_within_days`	No	Only people with a job change in the last N days.
`current_company_count_min`	No	Minimum number of concurrent current companies.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`items`	No	Array in the example

TDQS

A3.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering safety. The description adds genuinely useful behavioral context beyond annotations: cursor-based pagination and a 10-Zooq-credit cost, which are not visible in the schema or 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 one tight, front-loaded clause followed by a brief pagination note and a cost parenthetical. Every phrase earns its place and there is no redundant restatement of the tool name or schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives a solid high-level overview, and the rich annotations plus output schema cover safety and return structure. However, the tool has 33 optional parameters and a closely related sibling search_people_live; the description does not clarify the dataset-vs-live distinction or provide selection guidance, leaving an important gap for an agent choosing between siblings.

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?

Schema description coverage is 100%, so the schema already documents all 33 parameters. The description adds a useful high-level list of filter categories, but it does not add parameter-specific meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

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 description opens with a specific verb and object — 'Search professional records' — and enumerates the main filter dimensions (name, title, company, skills, education, tenure, geography). However, it does not explicitly differentiate this tool from sibling search_people_live or search_alumni, so the agent must infer which variant this is.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives. It does not mention search_people_live, search_alumni, or any exclusion criteria, leaving the agent without decision support for choosing among the search-family siblings.

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.

search_people

Input Schema

Output Schema

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