Apify Public Data & Leads

LinkedIn Public Job Listings Search

linkedin_jobs_search

Read-only

Search public LinkedIn job postings by keyword and location without login, returning title, company, location, date, and URL for hiring-trend research and talent-market mapping.

Instructions

Search public LinkedIn job postings by keyword and location without logging in, returning role title, hiring company, location, posting date, and job URL.

Behavioral Transparency:

Execution: Network call executed synchronously in the cloud via Apify Actor 'captainhandsome/linkedin-public-jobs-search'.
Side Effects: Reads public sources and creates a billed Actor run and dataset on your Apify account.
Authentication: Requires APIFY_TOKEN environment variable.
Latency & Limits: Typical run duration is 15-40 seconds; timeout capped at 120 seconds.

Usage Guidelines:

When to use: Use for hiring-trend research, talent-market mapping, competitor headcount signals, or sourcing public job openings by role and location.
When NOT to use: Do not use for LinkedIn people/profile search, private candidate data, or submitting job applications.
Named alternatives: Use 'glassdoor_jobs_search' for Glassdoor's own listings and employer ratings, or 'google_play_reviews_search'/'youtube_video_search' for app-review or video data instead of hiring data.

Input Schema

TableJSON Schema

Name	Required	Description
`location`	No	City, region, or country used to localize results (e.g. 'Seattle, WA'). Defaults to all locations if empty.
`max_results`	No	Maximum number of job listings to retrieve. Defaults to 10.
`search_query`	Yes	Job title, skill, or keyword to search in public LinkedIn job listings (e.g. 'data engineer' or 'registered nurse').
`include_details`	No	When true, opens each job posting to add seniority level, employment type, job function, industries, applicant count, and full job description. Costs one extra request per job, so runs take noticeably longer. Defaults to false.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`run`	No
`error`	No
`status`	Yes
`results`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed21 schema fields changedv1.1.0Sep 15, 2026

addedInput schema / additionalProperties
```
Added value: +false
```
addedOutput schema / properties / error
```
Added value: +{
+  "type": "object"
+}
```

removedOutput schema / properties / results / description

Removed value: -"Collection of public LinkedIn job listing records."

changedOutput schema / properties / results / items / properties / company / description

Previous value: -"Hiring company name."New value: +"Structured company value returned by the upstream source."

addedOutput schema / properties / results / items / properties / company / title
```
Added value: +"Company"
```

changedOutput schema / properties / results / items / properties / company_url / description

Previous value: -"URL of the hiring organization's LinkedIn page."New value: +"URL of the hiring organisation's LinkedIn page, with tracking parameters removed."

addedOutput schema / properties / results / items / properties / company_url / title
```
Added value: +"Company LinkedIn page"
```

changedOutput schema / properties / results / items / properties / job_id / description

Previous value: -"Numeric LinkedIn job posting ID, stable across runs."New value: +"Numeric LinkedIn job posting ID, stable across runs and usable as a join key."

addedOutput schema / properties / results / items / properties / job_id / title
```
Added value: +"LinkedIn job ID"
```

changedOutput schema / properties / results / items / properties / location / description

Previous value: -"Workplace location as displayed on the posting."New value: +"Structured location value returned by the upstream source."

addedOutput schema / properties / results / items / properties / location / title
```
Added value: +"Location"
```
addedOutput schema / properties / results / items / properties / posted_at / title
```
Added value: +"Posting date"
```

changedOutput schema / properties / results / items / properties / posted_at_timestamp / description

Previous value: -"posted_at parsed into a UTC ISO 8601 timestamp, when recognizable."New value: +"posted_at parsed into a UTC ISO 8601 timestamp. Null when posted_at is missing or not in a recognized absolute-date or relative-age ('3 days ago', 'Just now') shape."

addedOutput schema / properties / results / items / properties / posted_at_timestamp / title
```
Added value: +"Posting timestamp (ISO 8601)"
```

changedOutput schema / properties / results / items / properties / title / description

Previous value: -"Job posting title."New value: +"Human-readable title from the source."

addedOutput schema / properties / results / items / properties / title / title
```
Added value: +"Record title"
```

changedOutput schema / properties / results / items / properties / url / description

Previous value: -"Direct canonical URL to the job listing."New value: +"Direct URL for the source listing."

addedOutput schema / properties / results / items / properties / url / title
```
Added value: +"Canonical listing URL"
```
addedOutput schema / properties / run
```
Added value: +{
+  "type": "object"
+}
```

addedOutput schema / properties / status

Added value: +{
+  "enum": [
+    "success",
+    "empty_unverified",
+    "partial",
+    "error"
+  ],
+  "type": "string"
+}

changedOutput schema / required

Previous value: -[
-  "results"
-]New value: +[
+  "results",
+  "status"
+]

Addedv1.0.11Sep 13, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description goes well beyond the annotations by disclosing that execution is a synchronous network call via a specific Apify Actor, that it creates a billed Actor run and dataset on the user's Apify account, that it requires the APIFY_TOKEN environment variable, and that typical latency is 15-40 seconds with a 120-second timeout. This is exactly the kind of behavioral context (side effects, auth, latency) that annotations alone do not provide.

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?

The description is well-structured with clear sections for behavioral transparency and usage guidelines. It front-loads the core purpose in the first sentence, then uses labeled sections for additional context. Every sentence earns its place, and the formatting makes it easy for an agent to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete for a tool of this complexity. It covers purpose, execution model, side effects, authentication, latency, usage boundaries, and named alternatives. The output schema exists, so the description doesn't need to explain return values in detail. The only minor gap is that it doesn't mention pagination, but the max_results parameter and output schema cover the practical limits.

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 all four parameters. The description adds some context by mentioning the return fields and the 'without logging in' constraint, but it does not add significant meaning beyond the schema's parameter descriptions. Baseline 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?

The description states a specific verb ('Search'), a specific resource ('public LinkedIn job postings'), and the key constraints ('by keyword and location without logging in'). It also lists the exact return fields (role title, hiring company, location, posting date, job URL), which clearly distinguishes it from sibling tools like glassdoor_jobs_search and people-search tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides 'When to use' and 'When NOT to use' sections, including named alternatives like 'glassdoor_jobs_search' for Glassdoor listings and 'google_play_reviews_search'/'youtube_video_search' for non-hiring data. This gives an agent clear routing logic without needing to inspect sibling schemas.

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