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LinkedIn MCP Server

LinkedIn MCP Server

by Sabari2005
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

linkedin_get_job

Read-onlyIdempotent

Retrieve a LinkedIn job posting's full details: description, required skills, salary, applicant count, employment type, experience level, and hiring team. Use it to tailor a resume before applying.

Instructions

Read a job posting in full: complete description, required skills (extracted from the text), salary, applicant count, employment type, experience level and the hiring team where LinkedIn shows it. Use this before applying or tailoring a resume.

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": "<job>"
    },
    "name": "linkedin_get_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobYesWhich job: a numeric job id, a urn:li:jobPosting:… URN, or a linkedin.com/jobs/view/… URL. All three are accepted.
refreshNoBypass the cache and re-read.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.6

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark it read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds behavioral nuance by noting required skills are 'extracted from the text' (not a structured field) and that hiring team inclusion is conditional ('where LinkedIn shows it'). It also implies a cache via the refresh parameter, though the description itself doesn't mention caching; the schema does. Overall, the description adds meaningful context beyond annotations.

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 two sentences with no filler. The first sentence enumerates the specific data returned, and the second gives direct usage guidance. It is front-loaded with the core action ('Read a job posting in full') and efficiently structured.

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?

For a read tool with two parameters and no output schema, the description provides a comprehensive picture of what the agent will get (all major job fields) and when to use it. The lack of output schema is compensated by the detailed list of returned content. It is complete enough for an agent to select and invoke the tool correctly.

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 both parameters with detailed descriptions (job accepts id/URN/URL, refresh bypasses cache), so the schema does the heavy lifting. The tool description does not add extra meaning to the parameters beyond what's already in the schema. Since schema coverage is 100%, a baseline of 3 is appropriate.

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 reads a job posting in full, listing specific content fields (description, skills, salary, applicant count, employment type, experience level, hiring team). It also distinguishes it from sibling search tools by emphasizing full detail and provides a concrete use case ('before applying or tailoring a resume'). The verb 'Read' precisely identifies the operation.

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 explicitly says 'Use this before applying or tailoring a resume,' which gives a clear when-to-use context. It does not explicitly name alternatives or exclusions (e.g., 'use search_jobs for listings'), but the use case is unambiguous and sufficient for agent selection. The read-only nature is implicitly aligned with pre-application workflows.

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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