Skip to main content
Glama
Sundeepg98

linkedin-mcp

by Sundeepg98

linkedin_search_jobs

Search LinkedIn jobs by keywords, location, date, experience, and remote type. Retrieve one page of results with title, company, location, and link, and paginate manually via start offset.

Instructions

Search LinkedIn jobs with filters, returning one page of results.

Runs the search LinkedIn's own jobs page runs and reads the rendered results: title, company, location, job id and link.

One page load per call, no scrolling and no auto-paging -- LinkedIn puts roughly 25 results on a page, so ask for the next page deliberately with start=25, start=50 and so on. capped in the result tells you the limit trimmed the rows, and page_had tells you how many the page actually held.

Note that LinkedIn records searches in your own recent-search history, exactly as it would if you typed the query on the site. That is the only trace a search leaves, and it is on your account, not anyone else's.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNomaximum rows to return (default 25, max 50).
startNoresult offset for manual paging (0, 25, 50 ...).
remoteNoany | on_site | remote | hybrid.any
sort_byNorelevance | date.relevance
keywordsYeswhat to search for, e.g. "senior node.js engineer".
locationNocity, region or country. Empty means LinkedIn's default.
date_postedNoany | past_24h | past_week | past_month.any
experience_levelNocomma-separated from internship, entry, associate, mid_senior, director, executive. Empty means no filter.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full disclosure burden. It exceeds expectations by detailing that each call is a single page load with no scrolling or auto-paging, that results are roughly 25 per page, and that capped/page_had indicate row trimming. Most notably, it discloses a non-obvious side effect: searches are recorded in the user's own LinkedIn recent-search history, leaving a trace on the account. This level of transparency about side effects and pagination mechanics is exemplary.

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 structured into three focused paragraphs: purpose, pagination mechanics, and privacy note. It is front-loaded with the core purpose in the first sentence. Every sentence contributes either operational details (single page, start offsets) or behavioral transparency (capped, page_had, search history). It is concise relative to the amount of critical information it conveys, though slightly longer than the bare minimum. The structure makes the key usage guidance easy to find.

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?

For a search tool with 8 parameters and an output schema, the description covers all necessary aspects: what the tool does, how it works, how to page for more results, what the returned fields include (title, company, location, job id, link), and the side effect on search history. The presence of an output schema means the return format needn't be detailed, and the description still names the key fields. Nothing essential is missing for an agent to call this correctly.

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 baseline is 3. The description does not add significant meaning beyond the schema: it mentions start for paging and limit indirectly through the pagination discussion, but the schema already documents defaults, enums, and descriptions for every parameter (keywords, start, limit, remote, etc.). The description's pagination explanation adds marginal value over the schema's 'result offset for manual paging' line, but not enough to raise the score above baseline.

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 opens with a crisp statement of function: 'Search LinkedIn jobs with filters, returning one page of results.' It then specifics the returned fields (title, company, location, job id, link) and indicates the search runs on LinkedIn's own jobs page. This clearly distinguishes it from sibling tools like linkedin_job_detail (which likely retrieves a single job) and linkedin_saved_jobs (which lists saved jobs). The purpose is unambiguous and specific.

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 provides explicit operational guidance: it explains the single-page behavior and instructs the agent to page deliberately using start=25, start=50, etc. It also explains the meaning of capped and page_had fields for interpreting results. However, it does not explicitly say 'use this instead of X' for any sibling tool, though the distinct search function is implied. The guidance is clear on how to use the tool for repeated paginated searches, which is the core usage pattern.

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/Sundeepg98/linkedin-mcp'

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