Zooq

search_people_live

Read-onlyIdempotent

LIVE people search — the only endpoint that filters by current company, past company AND school together. Complements search_people (the deduplicated dataset, cursor-paginated, plain-string geo): use this one for company-history sourcing, that one for broad firmographic filtering. Offset-paginated. Not-found is free upstream. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description
`count`	No	Results per page, 0-50 (default 20).
`start`	No	Pagination offset, 0-999.
`title`	No	Job-title free-text filter.
`school`	No	Institution id(s), comma-separated. NOTE: this is the LIVE id namespace; the inst_ ids from g_institution_lookup are the dataset namespace and are not known to be interchangeable — unverified, treat with care.
`keyword`	No	Free-text keyword across the profile.
`industry`	No	Industry id(s), comma-separated. Takes an upstream id, NOT free text. No Zooq or upstream endpoint currently mints these ids, so free text is ignored (you get unfiltered results) — leave it unset until that changes. See ROADMAP F.
`lastName`	No	Last-name filter.
`firstName`	No	First-name filter.
`geoEntityId`	No	Geo entity id — this is LinkedIn's own public geo id, and it works. To find one: type your target city, state or country into the location box on LinkedIn search, pick the right match from the auto-complete dropdown, press enter to load the results page, then read `geoId=` followed by a long number out of the browser address bar (e.g. Israel = 101620260). Comma-separate for several. Resolve once and reuse it. Verified working on this endpoint. If you would rather filter by plain text, search_people takes geo_city / geo_country_code instead.
`pastCompany`	No	Numeric organization id(s), comma-separated — alumni sourcing: people who USED to work there. Same id source as currentCompany. This filter has no equivalent on search_people.
`currentCompany`	No	Numeric organization id(s), comma-separated — people who work there NOW. Mint the id with companies_entity_id (slug -> id), then reuse it.
`profileLanguage`	No	Profile primary language code, e.g. en.
`serviceCategory`	No	Service-category filter (for profiles offering services).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds meaningful behavioral context beyond that: offset pagination, free not-found upstream behavior, and a 10-Zooq-credit cost. These are operationally important details an agent would not know from annotations alone.

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: the first sentence names the tool and its unique selling point, the second distinguishes it from its sibling, and the remaining sentences add pagination and cost facts. Every sentence carries distinct, useful information with no filler.

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?

The description covers uniqueness, when to use this tool vs. search_people, pagination behavior, cost, and an important data-domain caveat. Combined with 100% parameter schema coverage, rich annotations, and an output schema, 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 input schema already has 100% description coverage for all 13 parameters, so the baseline is 3. The description adds semantic value by clarifying that currentCompany, pastCompany, and school can be combined together, and by framing pastCompany/currentCompany around 'company-history sourcing.' This goes slightly beyond the individual schema descriptions.

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 'LIVE people search' and states a precise, distinguishing capability: it is 'the only endpoint that filters by current company, past company AND school together.' It also explicitly contrasts itself with search_people, making the resource and function unmistakable.

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

Usage Guidelines5/5

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

The description gives direct routing guidance: 'use this one for company-history sourcing, that one for broad firmographic filtering.' It names the sibling alternative, explains the difference in dataset and pagination style, and leaves no ambiguity about when to pick this 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.

Resources

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