Skip to main content
Glama

agdata

LinkedIn job listings for a search

linkedin-jobs
Read-only

LinkedIn jobs search API for AI agents: send a job title or keywords and an optional location and get up to 50 public job listings as JSON: title, company, location, posting date, employment type, seniority, salary when listed, the listing's URL and its description. Filter by how recently a job was posted. Recruiter names are not returned. Jobs you paid for but did not get are refunded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNonumber of jobs wanted, 1 to 50 (default 10). A larger value is lowered to 50 and 0 or a negative one means the default; the quote follows the value used.
queryYesjob title, skill or company, e.g. "data engineer"
postedNoonly jobs posted within the last day (24h), week or month (default: any time)
locationNocity, region or country, e.g. "Prague" (default: anywhere)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld and non-destructive behaviour, so the description only needs to add context — and it does: results are capped at 50, only public listings are returned, recruiter names are never included, and billing is refunded for unfulfilled paid jobs. The cap and the public/recruiter caveats are behaviour an agent cannot infer from the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first clause, followed by return fields and constraints in one dense paragraph with no filler sentences. The billing/refund sentence is slightly tangential to tool selection but is short and self-contained.

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?

With no output schema, the description carries the return-value burden and discharges it by naming the JSON fields (title, company, location, date, type, seniority, salary, URL, description). Pagination behaviour beyond the 50-item cap and error/empty-result behaviour are unstated, which is the only real gap for a 4-parameter read tool.

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%, so the schema already documents limit clamping, the posted enum values and location format. The description only restates these at a high level (up to 50, filter by recency, optional location) without adding format or edge-case detail beyond the schema — baseline 3.

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 (LinkedIn jobs search) plus the exact retrieval scope: up to 50 public listings from a title/keyword plus optional location. It also enumerates the returned fields, so an agent can distinguish it from indeed-jobs (different source) and from the plain 'linkedin' profile tool without opening the schema.

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?

Clear context for when to reach for it: pass a job title or keywords, optional location, and optionally narrow by recency. It does not name alternatives (e.g. indeed-jobs) or say when this source is preferable, so the routing guidance stops short of explicit alternatives.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources