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Dev-Anandhan

LinkedIn MCP Server

by Dev-Anandhan

search_jobs

Search for jobs on LinkedIn using keywords, location, date, job type, experience, and work filters. Retrieve job IDs to access full details.

Instructions

Search for jobs on LinkedIn.

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

Args: keywords: Search keywords (e.g., 'backend developer', 'devops engineer') location: Optional location filter (e.g., 'Austin', 'Singapore') max_pages: Maximum number of result pages to load (1-10, default 3) date_posted: Filter by posting date (past_hour, past_24_hours, past_week, past_month) job_type: Filter by job type, comma-separated (full_time, part_time, contract, temporary, volunteer, internship, other) experience_level: Filter by experience level, comma-separated (internship, entry, associate, mid_senior, director, executive) work_type: Filter by work type, comma-separated (on_site, remote, hybrid) easy_apply: Only show Easy Apply jobs (default false) sort_by: Sort results (date, relevance)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sort_byNo
job_typeNo
keywordsYes
locationNo
max_pagesNo
work_typeNo
easy_applyNo
date_postedNo
experience_levelNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description must cover behavioral aspects. It explains that the tool returns job IDs but does not disclose rate limits, authentication needs, or whether it reads existing data without modification. This is a basic but acceptable level.

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?

The description is well-structured with a brief summary, return value note, and then a param list. It is slightly lengthy but each line adds value. Could be tightened slightly.

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?

Given the high parameter count (9) and many filter options, the description covers purpose, return value, and all parameters thoroughly. It lacks information about pagination or search scope but is otherwise complete for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description provides detailed guidance for each parameter, including example values for keywords and enumerated options for filters like job_type, experience_level, etc. This adds significant meaning beyond the raw schema.

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's purpose: 'Search for jobs on LinkedIn.' It also mentions that the return value (job_ids) can be passed to get_job_details, which differentiates it from other search tools like search_people.

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 implies when to use this tool (for job searching) and how to follow up with get_job_details. However, it does not explicitly state when not to use it or compare with alternatives like search_people.

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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