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View Saved Jobs

get_saved_jobs
Read-only

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). It shows the user's existing pipeline, so jobs they have already saved can be told apart from new ones. 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). A saved job the user can no longer see (its company is no longer starred, or a partner role was unpublished) keeps its entry but comes back with job: null, locked: true and a lockedReason to pass on. If job is null without locked, the listing was deleted entirely. A deactivated job may still be open on the company's careers page.

Input Schema

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / page / type
      Previous value: -"number"New value: +"integer"
    • changedInput schema / properties / pageSize / type
      Previous value: -"number"New value: +"integer"
  2. First observed

TDQS

A3.9/5.0
Behavior5/5

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

With readOnlyHint/destructiveHint already covering safety, the description goes well beyond annotations by decoding return-state semantics: isActive, deactivatedAt, job:null + locked:true + lockedReason vs a plain null meaning deletion, and the grace period caveat. This is exactly the behavioral context an agent cannot get from the schema.

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?

Purpose is front-loaded in sentence one and the following sentences are dense with return-state semantics rather than filler. It runs long for a two-parameter read tool, but with no output schema most of that length is earned, aside from mild overlap between the deactivation and 'may still be open' statements.

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 a read-only tool with no output schema, the description covers pagination behavior, job provenance, and all the null/locked return variants an agent needs. The main omission is disambiguation from get_starred_jobs and other listing siblings, which matters in this crowded namespace.

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% for both page and pageSize, so the schema already documents them fully. The description only adds the result ordering ('newest saves first'), which is a minor supplement to the schema rather than compensating detail; 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 first sentence gives a specific verb+resource ('Get the user's saved jobs list') and the rest clarifies scope (AI-extracted vs partner jobs, notes, job details). It never distinguishes itself from the near-named sibling get_starred_jobs or from get_feed/search_jobs, so the agent must infer 'saved' vs 'starred' on its own.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied via context ('the user's job search memory', 'shows the user's existing pipeline') rather than stated. There is no explicit when-to-use, when-not, or named alternative (e.g. get_starred_jobs, get_feed), leaving the agent to guess which listing tool fits.

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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