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Save Job

save_job

Save jobs to your personal list for later follow-up. Optionally attach notes to remember why the role interests you—persists across sessions.

Instructions

Save a job to the user's list for later. Supports both AI-extracted jobs (from get_starred_jobs) and internal partner jobs. This gives the agent memory across sessions — saved jobs persist so the user doesn't lose track of interesting roles. Optionally attach a note (e.g. "Great fit for React skills", "Follow up next week"). Each job can only be saved once.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "jobId": "<jobId>"
    },
    "name": "save_job"
  }
}

Input Schema

Name	Required	Description
`jobId`	Yes	The ID of the job to save
`jobType`	No	Type of job: "ai_job" (from get_starred_jobs, default) or "listing_job" (internal partner job)
`note`	No	Optional note about why this job is interesting

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0May 28, 2026

TDQS

A4.6/5.0

Behavior5/5

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

The description discloses key behaviors: jobs persist across sessions and each job can only be saved once. Annotations do not cover these constraints, so the description adds significant value. No contradictions with annotations (readOnlyHint=false is consistent with a write operation).

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 four sentences with no extraneous words. It front-loads the main purpose, uses natural language, and every sentence adds value (purpose, sources, persistence, note, uniqueness constraint).

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?

The description adequately covers the tool's role among siblings, explains the unique constraint, and references relevant sources. It lacks explicit error information or success confirmation, but given no output schema, this is acceptable and unlikely to confuse an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds context by explaining that jobType relates to get_starred_jobs for ai_job and partner jobs for listing_job, and provides an example for the note parameter. This goes beyond the schema's descriptions.

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 states the tool saves a job to the user's list, distinguishing it from siblings like apply_to_job or unsave_job. It specifies the two supported job types (AI-extracted and internal partner jobs), leaving no ambiguity about its purpose.

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 explains when to use the tool (save jobs for later) and references the two job sources (get_starred_jobs and partner jobs). It does not explicitly state when not to use it or provide alternatives, but the context is clear given sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.