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kaistenberg

MCP Server for LinkedIn

by kaistenberg

Search Jobs

search_jobs
Read-only

Search LinkedIn jobs with keywords and filters (location, job type, work type, experience level, date) to obtain job IDs for fetching full job 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
sort_byNoSort results (date, relevance)
job_typeNoFilter by job type, comma-separated (full_time, part_time, contract, temporary, volunteer, internship, other)
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)
work_typeNoFilter by work type, comma-separated (on_site, remote, hybrid)
easy_applyNoOnly show Easy Apply jobs (default false)
date_postedNoFilter by posting date (past_hour, past_24_hours, past_week, past_month)
experience_levelNoFilter by experience level, comma-separated (internship, entry, associate, mid_senior, director, executive)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already cover readOnlyHint=true, which is reinforced by the description's 'Search' action. The description adds behavioral context by explicitly stating that results contain only job_ids and that full details require a separate call, which helps set expectations about the return value.

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 concise sentences, front-loaded with the main purpose and a valuable cross-reference to get_job_details. No filler, every word earns its place.

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 rich schema (100% parameter coverage) and presence of an output schema, the description provides sufficient context. It could mention pagination via max_pages, but the schema already covers that, so the description remains complete for effective tool selection and invocation.

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%, with all 9 parameters having clear descriptions in the schema. The tool description itself adds no parameter-specific semantics, so the baseline score 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?

Description clearly states 'Search for jobs on LinkedIn' with a specific verb and resource, distinguishing it from sibling tools like search_people and search_companies. It also specifies the output format (job_ids) and points to get_job_details for full info, which further clarifies the tool's role.

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 (job searching) and guides the agent to get_job_details for full job information. However, it does not explicitly name alternative tools or state when not to use it, though the sibling names and context make this reasonably clear.

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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