Fresh Linkedin Profile Data MCP Server

Get Job Details

get_job_details

Retrieve complete job details from a LinkedIn job URL, including company information. Optionally add skills and hiring team details.

Instructions

Scrape the full job details, including the company basic information. 1 credit per call.

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_url": "<job_url>"
    },
    "name": "get_job_details"
  }
}

Input Schema

Name	Required	Description
`job_url`	Yes	Example value: https://www.linkedin.com/jobs/view/3766410207/
`include_skills`	No	Including skills will cost 1 more credit
`include_hiring_team`	No	Including hiring team information will cost 1 more credit

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Sep 4, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool 'scrapes' data and mentions the credit cost, which is useful. However, it does not describe output format, latency, failure behavior, or any other operational characteristics.

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?

Two short sentences with no filler. The core action and scope are front-loaded, and the cost detail is a useful addition without bloating the description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is adequate for a simple job-details scrape with a single required parameter. However, with no output schema or behavioral annotations, it could be more complete by describing what fields are returned or how the optional parameters affect the result.

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?

The input schema covers all three parameters with descriptions, so the baseline is 3. The description adds little beyond the schema, only mentioning that company basic information is included in the result, not deepening parameter understanding.

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 states a specific verb ('scrape') and a specific resource ('full job details'), and clarifies it includes company basic information. This clearly distinguishes it from sibling tools like search_jobs or get_post_details.

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

Usage Guidelines2/5

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

No guidance is given about when to use this tool versus alternatives. There is no mention of prerequisites, such as having a job URL, nor any comparison to similar job-related tools.

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

Deploy Server