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View Saved Jobs

get_saved_jobs
Read-only

Retrieve your saved jobs with notes and deactivation status to review your pipeline and avoid re-recommending bookmarked or dismissed listings.

Instructions

Get the user's saved jobs list. This is the user's job search memory — shows all jobs they've bookmarked across sessions, with notes and job details. Includes both AI-extracted jobs and internal partner jobs (see jobType field). IMPORTANT: Always call this before get_starred_jobs when helping with job search — it shows the user's existing pipeline so you can avoid re-recommending jobs they've already saved or dismissed. Paginated, newest saves first. Each saved job includes isActive and deactivatedAt fields. If isActive is false, the job is no longer appearing on the company's careers page — this usually means it was filled or expired, but could also be a temporary scraper issue (there is a grace period before deactivation). If job is null, the listing was deleted entirely. Suggest the user check the company's careers page directly if a saved job they care about gets deactivated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, starting from 1 (default: 1)
pageSizeNoResults per page, max 50 (default: 20)
Behavior5/5

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

Adds significant behavioral details beyond annotations: pagination sorted newest first, explanation of isActive/deactivatedAt fields, and handling of null jobs. Annotations already indicate read-only, so description enriches further.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with key purpose, then additional context. Every sentence adds value, though the description could be slightly tighter. Good length given richness.

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?

Very complete for a read-only list tool: includes pagination details, field explanations, edge cases (null job, deactivation), and actionable advice. No output schema needed due to thorough description.

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 covers 100% of parameters with descriptions. Description adds no extra parameter meaning beyond what schema provides, so baseline 3 is appropriate.

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?

Explicitly states 'Get the user's saved jobs list' and clarifies it's the user's job search memory, distinguishing it from siblings like get_starred_jobs.

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?

Clearly instructs to call this before get_starred_jobs and explains why (avoid re-recommending saved jobs). Lacks explicit when-not, but given the context, the guidance is strong.

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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