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Get job details

get_job_details
Read-onlyIdempotent

Fetch one LinkedIn job posting: title, description, location, seniority, employment and workplace type, apply links, application counts and the hiring company.

Costs 1 credit. include_hiring_team adds 1 more, charged only when the hiring team is actually returned.

Give either job_id (the numeric id) or url (any LinkedIn job URL — the id is extracted for you). One of the two is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
job_idNo
include_hiring_teamNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Adds real cost behavior beyond the annotations: base cost of 1 credit, plus 1 for `include_hiring_team` charged only when the hiring team is actually returned. Annotations already cover read-only/idempotent/open-world semantics, so this is a meaningful addition. It doesn't describe failure behavior for invalid ids or URLs.

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?

Three short, front-loaded blocks: content returned, cost model, argument contract. No filler sentences, and the critical cost/argument constraints appear before any detail.

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?

An output schema exists, so return-value documentation is not required, and the description covers cost and the required-argument constraint. Minor gaps remain around invalid-input behavior and the schema typing of `job_id` as a string despite being called numeric.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate, and it does: `job_id` is the numeric id, `url` accepts any LinkedIn job URL with the id extracted automatically, and `include_hiring_team` toggles an optional extra-cost payload. All three parameters are given meaning absent from the schema.

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?

States a specific verb and resource ('Fetch one LinkedIn job posting') and enumerates the returned fields (title, description, location, seniority, employment/workplace type, apply links, application counts, hiring company). No sibling tool retrieves a single job, so the scope is unambiguous against the surrounding search/get tools.

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?

Gives clear invocation guidance: supply either `job_id` or `url`, one of the two is required. It does not state when to prefer this over a search tool (e.g., after obtaining an id from search results), but no sibling actually competes for this function.

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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