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Search Linkedin Jobs

search_linkedin_jobs

Search LinkedIn's public job postings by keywords and location, returning matched jobs with title, company, location, URL, and posted date. Use it to find current job listings without logging in.

Instructions

Search LinkedIn public job postings and return a list of matched jobs.

Scrapes LinkedIn's public job search results over the network (no login required) and returns a list of dicts, one per posting, typically with keys: title, company, location, url, and posted_date. Returns an empty list if no postings match or the query yields no results. Live web scraping, so results reflect LinkedIn at call time and may vary between runs.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	Job title or keywords as a string. Example: "machine learning engineer". No default (required).
`location`	No	Location filter as a string; city, region, or country. Example: "London" or "United Kingdom". No default (required).
`max_pages`	No	Number of result pages to scrape, as an integer. Each extra page adds jobs but more scraping time. Example: 2. Default: 1.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`count`	No
`errors`	No
`scraper`	No
`source_urls`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedJul 31, 2026

changedInput schema / properties / location / description

Previous value: -"Location filter, e.g. \"London\" or \"United Kingdom\"."New value: +"Location filter as a string; city, region, or country. Example: \"London\" or \"United Kingdom\". No default (required)."

changedInput schema / properties / max_pages / description

Previous value: -"Pages of results to scrape (default 1)."New value: +"Number of result pages to scrape, as an integer. Each extra page adds jobs but more scraping time. Example: 2. Default: 1."

changedInput schema / properties / query / description

Previous value: -"Job title or keywords, e.g. \"machine learning engineer\"."New value: +"Job title or keywords as a string. Example: \"machine learning engineer\". No default (required)."

Changed20 schema fields changedv1.3.3Jul 31, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

addedInput schema / properties / location / description

Added value: +"Location filter, e.g. \"London\" or \"United Kingdom\"."

removedInput schema / properties / location / title
```
Removed value: -"Location"
```

addedInput schema / properties / max_pages / description

Added value: +"Pages of results to scrape (default 1)."

removedInput schema / properties / max_pages / title
```
Removed value: -"Max Pages"
```

addedInput schema / properties / query / description

Added value: +"Job title or keywords, e.g. \"machine learning engineer\"."

removedInput schema / properties / query / title
```
Removed value: -"Query"
```

removedInput schema / title

Removed value: -"search_linkedin_jobsArguments"

removedOutput schema / $defs

Removed value: -{
-  "ToolError": {
-    "description": "A non-fatal problem encountered while scraping.",
-    "properties": {
-      "message": {
-        "title": "Message",
-        "type": "string"
-      },
-      "url": {
-        "title": "Url",
-        "type": "string"
-      }
-    },
-    "required": [
-      "url",
-      "message"
-    ],
-    "title": "ToolError",
-    "type": "object"
-  }
-}

removedOutput schema / properties / count / title
```
Removed value: -"Count"
```
removedOutput schema / properties / data / title
```
Removed value: -"Data"
```
removedOutput schema / properties / errors / items / $ref
```
Removed value: -"#/$defs/ToolError"
```

addedOutput schema / properties / errors / items / description

Added value: +"A non-fatal problem encountered while scraping."

addedOutput schema / properties / errors / items / properties

Added value: +{
+  "message": {
+    "type": "string"
+  },
+  "url": {
+    "type": "string"
+  }
+}

addedOutput schema / properties / errors / items / required
```
Added value: +[
+  "url",
+  "message"
+]
```
addedOutput schema / properties / errors / items / type
```
Added value: +"object"
```
removedOutput schema / properties / errors / title
```
Removed value: -"Errors"
```
removedOutput schema / properties / scraper / title
```
Removed value: -"Scraper"
```
removedOutput schema / properties / source_urls / title
```
Removed value: -"Source Urls"
```
removedOutput schema / title
```
Removed value: -"ScrapeToolResult"
```

Addedv1.2.0Jul 26, 2026
Removedv1.2.0Jul 26, 2026
AddedJul 26, 2026

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses live network scraping, no-login operation, call-time variability, the expected return structure ('list of dicts... typically with keys'), and empty-list behavior. This is far more transparent than most tool descriptions.

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?

Three focused sentences with the core purpose front-loaded. Every sentence earns its place by adding behavioral or outcome detail without repeating schema content or padding.

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?

Given no annotations, the description still supplies the key context an agent needs: operation type, network dependence, authentication requirement, result format, and empty-result behavior. The output schema is also marked as present, and pagination is handled by the schema. The description is complete enough for correct 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 coverage is 100%, so the input schema already documents query, location, and max_pages with examples and defaults. The prose description adds no additional parameter-level meaning, so the baseline score of 3 applies.

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 an unambiguous action ('Search LinkedIn public job postings') and a concrete outcome ('return a list of matched jobs'). This clearly differentiates from generic scrapers and sibling search tools by naming the specific resource and domain.

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?

Provides clear context: the tool is for public LinkedIn job postings, works over the network with no login required, and returns live results. It does not explicitly name alternatives or exclusion conditions, but the domain-specific scope leaves little ambiguity about when to use it.

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