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ZOOQ - LinkedIn Data for AI Agents

jobs_similar

Read-onlyIdempotent

Similar job postings (title, organization, location, salary range, posted date). (Costs 10 Zooq credits.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opportunityEntityIdYesNumeric job posting id. Get it from search_jobs — read data.jobs[].id — or companies_jobs — read data.jobs[].jobID.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalNoExample value was a number
opportunitiesNoArray in the example

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior, and the description adds the non-obvious cost of 10 Zooq credits plus the specific output fields returned. This is useful context beyond the structured metadata and contains no contradiction.

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 a compact two-part sentence: it states the result in the opening clause and appends the cost parenthetically. Every element earns its place, with no filler or repetition.

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 a one-parameter, read-only tool with an output schema and rich annotations, the description plus schema covers purpose, input source, returned fields, and cost. Nothing essential for an agent to select and invoke the tool correctly 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?

The input schema already documents opportunityEntityId fully, including sourced path examples from search_jobs and companies_jobs. The description itself adds no further parameter-level meaning, so the baseline 3 applies.

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 identifies the tool's output as 'similar job postings' with concrete fields (title, organization, location, salary range, posted date), which clearly distinguishes it from siblings like jobs_details_v2 or jobs_people_also_viewed. It lacks an explicit verb, but the title 'Find similar jobs' and the resource context make the purpose unmistakable.

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 choose jobs_similar over alternatives such as jobs_details_v2 or jobs_people_also_viewed. It only implies usage through the name and purpose; the source instructions for opportunityEntityId live in the schema, not the description.

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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