ZOOQ - LinkedIn Data for AI Agents

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.)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "search_job_changes"
  }
}

Input 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

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

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already establish read-only, open-world, idempotent, and non-destructive behavior. The description adds value by disclosing that results are page-paginated and that the tool costs 10 Zooq credits, which are useful operational details not present in the 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?

Three concise sentences deliver the core subject, pagination behavior, intended use case, and cost. Every sentence earns its place, with no filler or redundancy.

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 an open-world, read-only search tool with an output schema and fully documented optional parameters, the description covers the essentials: what events are included, how results are paginated, when to use it, and the cost. Nothing an agent needs to decide whether to invoke it 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?

Parameter schema coverage is 100%, so the schema already documents all eight parameters. The description contributes contextual meaning by enumerating the event types and noting pagination, but it does not add meaningful details about parameter values or constraints beyond the schema.

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 identifies the resource as recent professional job-change events and specifies the three event categories (joined, left, changed titles). It distinguishes this from generic people/job search siblings by focusing on organizational employment events, so an agent can understand exactly what the tool returns.

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 explicitly states the intended use case: 'trigger-based prospecting and territory monitoring.' It gives clear context for when this tool is appropriate, though it does not name alternatives or state when not to use it, so it stops one point short of the highest level.

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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