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linkedin-job-mcp

linkedin-job-mcp

check_ghost_job

Grades a job posting for ghost-job risk, estimates your chance against competition, and explains the evidence behind the grade.

Instructions

Grade a job posting with Ghost Job Radar: how likely it is to be a ghost job (not really open), your chance against the competition, and the facts behind the grade. Needs the radar running (linkedin-jobs-mcp radar)

Input Schema

NameRequiredDescriptionDefault
jobYesJob link (LinkedIn, the employer's site or a job board) or the pasted ad text
companyNoCompany name, when the job is pasted text
with_newsNoAlso check company news (slower)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

A3.8/5.0
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 behavioral burden. It discloses the radar prerequisite and summarizes the outputs, but it does not state whether the operation is read-only, what happens on failure, or any rate/latency constraints beyond the schema's with_news note.

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 sentences, front-loaded with the action and outputs, with the prerequisite appended without excess. It does not waste space restating schema details.

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?

No output schema exists, so the description should convey return values; it does at a high level by naming ghost likelihood, competition chance, and supporting facts. Combined with full schema coverage and the radar prerequisite, it is mostly complete, though it omits failure and availability details.

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 coverage is 100%, so the schema already documents job, company, and with_news. The description adds no parameter-level meaning beyond saying it grades a job posting, 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb, "Grade," and resource, "job posting," and names the distinct outputs: ghost-job likelihood, competition chance, and supporting facts. This clearly differentiates it by function from the sibling detail and search tools, even without naming them.

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?

Provides a prerequisite that the radar must be running and implies the tool is for ghost-job assessment. However, it does not explicitly say when to choose it over get_linkedin_job_details or search_linkedin_jobs, nor when not to use it.

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