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get job

get_job
Read-onlyIdempotent

Show one role's posting text, pay, stage and fit (with the keywords it matches and misses), for a tracked role or one from the last scan, named by company and title. Use it to answer questions about a specific posting or before tailoring; for the list of tracked roles use list_pipeline, and for a public link nobody has tracked use read_page. With no title and several tracked roles at that company, it lists them so you can ask which. A tracked role with no fit yet is scored once with the user's AI and the score saved; otherwise it changes nothing.

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": {
      "company": "<company>"
    },
    "name": "get_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleNoThe role's title as the pipeline lists it; leave it empty when the user named only the company
companyYesThe employer's name, e.g. "Stripe"

Output Schema

NameRequiredDescriptionDefault
textYesThe answer, or what was done, in plain words for the user: the same text as content
undoNoOn a saved write: its undo handle, for undo within 24 hours
widgetNoThe card the host draws with it, e.g. "pipeline"; "confirm" means a write waits for the user's Yes and nothing is saved yet

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior4/5

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses a non-obvious side effect: a tracked role with no fit is scored once with the user's AI and the score is saved, otherwise nothing changes. That is exactly the kind of cached-write context annotations cannot convey, and it usefully qualifies the readOnlyHint. It stops short of describing latency or cost of that scoring call, which keeps it at 4.

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?

It is front-loaded with the payload description, then alternatives, then edge cases, and every clause carries information. The opening sentence is heavily nested with parentheticals and the fallback/side-effect sentences run long, so it is dense rather than wasteful, but not maximally tight.

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 an output schema present, return values need not be described, and the description covers the remaining agent-relevant facts: data sources (tracked vs last scan), the missing-title fallback, the one-time scoring side effect, and routing to siblings. Nothing needed to call this correctly is absent.

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, but the description adds meaning the schema does not: title is optional and should be left out when the user names only a company, and omitting it triggers a list-then-ask fallback rather than an error. The company+title lookup key is also made explicit.

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 opens with a specific verb and a concrete resource set: 'Show one role's posting text, pay, stage and fit (with the keywords it matches and misses)'. It scopes the resource to 'a tracked role or one from the last scan, named by company and title', which lets an agent distinguish it from list_pipeline and read_page without opening any schema.

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

Usage Guidelines5/5

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

It gives explicit when-to-use ('to answer questions about a specific posting or before tailoring') and names two alternatives with the conditions that select them: list_pipeline for the list of tracked roles, read_page for a public link nobody has tracked. It also covers the disambiguation branch when title is omitted and several roles exist at one company.

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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