job-mcp

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get_linkedin_job_details

Fetch complete details for a LinkedIn job posting using its job ID, including role, company, and application information for job research or tracking.

Instructions

Retrieve full job details for a specific LinkedIn job posting.

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": {
      "job_id": "<job_id>"
    },
    "name": "get_linkedin_job_details"
  }
}

Input Schema

Name	Required	Description	Default
`job_id`	Yes	LinkedIn job ID (e.g. 'linkedin_4152839402' or '4152839402').

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 5, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states it retrieves details but does not disclose whether this is a read-only operation, authentication requirements, rate limits, or error behavior. The presence of an output schema reduces the need to describe return values, but key behavioral traits remain undisclosed.

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?

A single, front-loaded sentence that is appropriately sized and contains no unnecessary information.

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 tool is simple with one parameter, and an output schema exists. The description is complete enough for an agent to understand what it does and how to call it, though it could mention that the job_id must be valid or that authentication is required.

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%, and the schema already documents the job_id parameter with examples. The description adds no additional meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 description clearly states a specific verb 'Retrieve' and resource 'full job details for a specific LinkedIn job posting', distinguishing it from search-oriented siblings like search_linkedin_jobs. It is clear but does not explicitly mention which sibling to use when only a summary is needed.

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 implied: use when you have a specific job ID and need full details. However, there is no explicit guidance on when to use this versus search_linkedin_jobs or other retrieval tools, leaving some inference to the agent.

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