Skip to main content
Glama

Search Jobs

search_jobs
Read-only

Search LinkedIn jobs by keywords, location, and filters including date posted, job type, experience level, work type, and easy apply. Returns job IDs for full details.

Instructions

Search for jobs on LinkedIn.

Returns job_ids that can be passed to get_job_details for full info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYesSearch keywords (e.g., "software engineer", "data scientist")
locationNoOptional location filter (e.g., "San Francisco", "Remote")
max_pagesNoMaximum number of result pages to load (1-10, default 3)
date_postedNoFilter by posting date (past_hour, past_24_hours, past_week, past_month)
job_typeNoFilter by job type, comma-separated (full_time, part_time, contract, temporary, volunteer, internship, other)
experience_levelNoFilter by experience level, comma-separated (internship, entry, associate, mid_senior, director, executive)
work_typeNoFilter by work type, comma-separated (on_site, remote, hybrid)
easy_applyNoOnly show Easy Apply jobs (default false)
sort_byNoSort results (date, relevance)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description need not repeat those. It adds the useful behavioral detail that only job_ids are returned. However, it does not disclose pagination behavior (max_pages) or ordering (sort_by), which are relevant but are covered by the schema.

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 with zero wasted words. The first sentence states the core purpose, the second describes the output and its intended use. Front-loaded and efficient.

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 covers the primary purpose and output but omits nuanced details like pagination limits (though max_pages is in schema) or result ordering. With an output schema present, the bare-bones description is minimally acceptable but could be more complete.

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%, meaning all 9 parameters are fully described in the input schema. The description adds no new parameter semantics beyond stating that the output is a list of job_ids. This is adequate but does not enhance understanding of parameter usage.

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 explicitly states 'Search for jobs on LinkedIn' providing a clear verb and resource. It also distinguishes from sibling search tools by mentioning the return type (job_ids) and the follow-on tool get_job_details, making its purpose and unique output unmistakable.

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 clearly indicates that results (job_ids) are meant to be passed to get_job_details for full info, implying a workflow. While it does not formally state when not to use this tool, the context of sibling search tools and the explicit output hint provide sufficient guidance for typical usage.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/stickerdaniel/linkedin-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server