Zooq

search_job_changes

Read-onlyIdempotent

Recent professional job-change events — people who joined, left, or changed titles at organizations. Page-paginated. Built for trigger-based prospecting and territory monitoring. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No	Page number (>=1, default 1).
`limit`	No	Results per page, 1-50 (default 20).
`title`	No	Partial job-title filter (min 3 chars).
`days_ago`	No	Recency window in days (1-365). Omit for no window.
`geo_city`	No	City filter (min 3 chars).
`event_type`	No	Filter by event. Accepted values: joined, left, title_change.
`geo_country_code`	No	ISO country code filter, e.g. us.
`organization_ids`	No	Comma-separated stable org_ ids to watch. Get them from companies_universal_name_to_id (read data.id).

Output Schema

TableJSON Schema

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

TDQS

A4.2/5.0

Behavior4/5

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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral details beyond those: results are page-paginated, the tool costs 10 Zooq credits, and the returned data consists of job-change events.

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, opening with the core purpose and then adding pagination, use case, and cost. Every sentence carries useful information without fluff or repetition.

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

Completeness4/5

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

Given the 100% schema coverage, annotations, and output schema, the description is nearly complete. It covers purpose, event types, pagination, intended use, and cost; only an explicit comparison to sibling tools is missing.

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 parameters are fully documented in the input schema. The description adds no detailed parameter semantics beyond what the schema already provides, which merits the baseline score of 3.

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 defines the resource as recent professional job-change events and names the specific event types: joined, left, or changed titles. It is easily distinguished from siblings like search_people or search_jobs because it is an event-stream tool rather than a person or job search.

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 states an explicit use case: 'Built for trigger-based prospecting and territory monitoring.' This provides clear context for when the tool is appropriate, though it does not explicitly name alternatives or exclusion conditions.

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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