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ZOOQ - LinkedIn Data for AI Agents

companies_jobs

Read-onlyIdempotent

Open job postings across one or more organizations. (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startNoPagination offset.
organizationEntityIdsYesComma-separated NUMERIC organization ids, e.g. 1035 (page size fixed at 50). Get each one from companies_entity_id. org_ ids from /companies/universal-name-to-id are silently ignored upstream — the response comes back UNFILTERED, so never pass them. This endpoint takes a LIST, so slugs are not auto-resolved here; resolve them first.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsNoArray in the example
countNoExample value was a number
startNoExample value was a number
totalNoExample value was a number
hasMoreNoExample value was a boolean

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses a 10 Zooq credit cost, and the schema warns about silently ignored org_ IDs yielding unfiltered responses, fixed page size of 50, and no slug auto-resolution. These are valuable behavioral details an agent needs to avoid incorrect usage.

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 two short sentences with no filler. The core purpose is front-loaded, and the credit cost is appended as a concise parenthetical, making every word earn its place.

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?

With a complete input schema, a rich output schema, and careful warnings about ID resolution and unfiltered responses, nothing essential is missing for an agent to select and invoke this tool correctly. The description alone is minimal, but the overall definition is contextually complete.

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 input schema fully documents organizationEntityIds and start, including format, example, and pagination semantics. The main description adds no extra parameter meaning, but the baseline of 3 applies because the schema carries the load completely.

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 states a clear verb and resource: retrieving open job postings across one or more organizations. This immediately distinguishes it from job-detail or job-search siblings and aligns with the title 'List company job openings'.

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 main description makes the use case clear — listing jobs by organization — and the schema adds strong guidance on sourcing numeric IDs from companies_entity_id and avoiding universal-name-to-id IDs. It does not explicitly name an alternative tool or state when not to use it, so it falls just short of a 5.

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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