linkedin-job-mcp

LinkedIn Jobs MCP

LinkedIn job search and full job details for your AI apps, plus Ghost Job Radar, a dashboard that shows which job postings are likely not really open. A Windows app connects itself to Claude Code, Codex, VS Code and other MCP clients; the same tools also come as an npm package, a REST API and a command line.

Tools

Tool	What your AI app gets
search_linkedin_jobs	Jobs by keyword and location, filtered by date, job type, remote work, salary, experience level, verified postings or fewer than 10 applicants
get_linkedin_job_details	One job's full description, seniority, employment type, industries, posting age and applicant count
check_ghost_job	Ghost Job Radar's grade for one posting: how likely it is a ghost job, your chance against the competition, and the facts behind it. Needs the radar running

It reads LinkedIn's public job pages, so it needs no account and no browser. Requests are spaced out and cached.

Ghost Job Radar

A dashboard on your computer that grades every job it sees for ghost-job risk: postings that stay up far longer than usual for their occupation, reposts with unchanged text, jobs gone from the employer's own site, start dates long past, talent-pool wording, layoff news while still hiring, and what you know about the employer. Every job gets three separate verdicts: ghost-risk, your chance, and whether the employer answers.

linkedin-jobs-mcp radar

With the Windows app, choose 5 in its menu or run .\linkedin-jobs-mcp.exe radar. It needs Python 3.11 or newer. The first start sets up the radar's Python environment; every start also runs the LinkedIn source, so LinkedIn search and job pages work right away. Your data stays on your computer, in %LOCALAPPDATA%\linkedin-jobs-mcp\radar (macOS: ~/Library/Application Support/linkedin-jobs-mcp/radar, Linux: ~/.local/share/linkedin-jobs-mcp/radar). A .env file there switches on more sources, such as the Bundesagentur job board or Google for Jobs; radar/README.md explains every view, signal and source.

Install

Windows app

1. Download linkedin-jobs-mcp.exe from the latest release. It is one file with Node.js inside.
2. Double-click it. The file is not code-signed, so Windows may say "Windows protected your PC": choose More info → Run anyway.
3. Choose 1 Install and connect the apps found. The app copies itself to %LOCALAPPDATA%\Programs\linkedin-jobs-mcp\ and adds itself to every supported app it finds.
4. Restart those apps. The app has no window of its own: they start it in the background when they need the tools.

npm (Windows, macOS, Linux)

Needs Node.js 18 or newer.

npm install -g linkedin-jobs-mcp
linkedin-jobs-mcp connect

Or add it to an app by hand, through npx:

{
  "mcpServers": {
    "linkedin-jobs": { "command": "npx", "args": ["-y", "linkedin-jobs-mcp"] }
  }
}

On Windows, some apps need "command": "cmd", "args": ["/c", "npx", "-y", "linkedin-jobs-mcp"].

From source

git clone https://github.com/GhoshSrinjoy/linkedin-job-mcp.git
cd linkedin-job-mcp
npm install
npm run app -- connect

Connect your apps

App	Name for connect	Where the setting goes
Claude Code	claude-code	claude mcp add --scope user
Codex	codex	~/.codex/config.toml
VS Code	vscode	%APPDATA%\Code\User\mcp.json
VS Code Insiders	vscode-insiders	%APPDATA%\Code - Insiders\User\mcp.json
Claude Desktop	claude-desktop	%APPDATA%\Claude\claude_desktop_config.json
Cursor	cursor	~/.cursor/mcp.json
Windsurf	windsurf	~/.codeium/windsurf/mcp_config.json
Gemini CLI	gemini-cli	~/.gemini/settings.json
LM Studio	lm-studio	~/.lmstudio/mcp.json
Cline	cline	%APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json

connect only adds or replaces the linkedin-jobs entry: other servers and settings stay, and the first change keeps a copy of the file next to it (<file>.linkedin-jobs.bak). A settings file with comments is left unchanged, and the entry to add is printed instead.

Commands

Command	What it does
linkedin-jobs-mcp	The setup menu in a console window; the MCP server when an app starts it
linkedin-jobs-mcp install	Copy the Windows app to %LOCALAPPDATA%\Programs\linkedin-jobs-mcp\ and connect the apps found
linkedin-jobs-mcp connect [app ...]	Connect the apps found, or only the named ones (also when they are not found)
linkedin-jobs-mcp disconnect [app ...]	Remove the connection from every app, or only from the named ones
linkedin-jobs-mcp status	Which apps are found and connected
linkedin-jobs-mcp config [app ...]	Print the settings to add by hand
linkedin-jobs-mcp serve [--port 3000] [--host 127.0.0.1]	REST API plus MCP over HTTP
linkedin-jobs-mcp mcp	MCP over stdio (what the connected apps run)
linkedin-jobs-mcp job <id or URL>	One job's details as JSON
linkedin-jobs-mcp search --keyword "data engineer" --location "Berlin, Germany"	Search from the command line
linkedin-jobs-mcp radar [--port 8000] [--no-browser]	Open Ghost Job Radar
linkedin-jobs-mcp uninstall	Disconnect every app and remove the installed Windows app

With the Windows app, run them from its folder, for example .\linkedin-jobs-mcp.exe status.

Add it by hand

Every app needs the same thing: the program, started with the mcp argument. VS Code (mcp.json):

{
  "servers": {
    "linkedin-jobs": {
      "type": "stdio",
      "command": "C:\\Users\\you\\AppData\\Local\\Programs\\linkedin-jobs-mcp\\linkedin-jobs-mcp.exe",
      "args": ["mcp"]
    }
  }
}

Claude Desktop, Cursor, Windsurf, Gemini CLI, LM Studio and Cline take the same entry under "mcpServers", without "type". Codex (config.toml):

[mcp_servers.linkedin-jobs]
command = "C:\\Users\\you\\AppData\\Local\\Programs\\linkedin-jobs-mcp\\linkedin-jobs-mcp.exe"
args = ["mcp"]

Claude Code:

claude mcp add --scope user linkedin-jobs -- "C:\Users\you\AppData\Local\Programs\linkedin-jobs-mcp\linkedin-jobs-mcp.exe" mcp

Ollama, through mcp-client-for-ollama: save the mcpServers entry in a file and start ollmcp -j servers.json.

linkedin-jobs-mcp config prints all of these with the real path.

MCP over HTTP

Apps that connect by URL can use the HTTP server instead. Start linkedin-jobs-mcp serve (or npm run api) and add http://127.0.0.1:3000/mcp:

claude mcp add --transport http linkedin-jobs http://127.0.0.1:3000/mcp

{ "servers": { "linkedin-jobs": { "type": "http", "url": "http://127.0.0.1:3000/mcp" } } }

The server listens on this computer only and refuses requests that name another host, so web pages cannot reach it through DNS rebinding. HOST=0.0.0.0 makes it reachable from other machines, for example from a container; protect it yourself then.

Search options

Option	Values
keyword	Job title or keywords, e.g. data engineer
location	City, region or country, e.g. Berlin, Germany
city, country	Narrow the location
geoId	LinkedIn location id, skips the location lookup
dateSincePosted	past 24 hours, past week, past month
jobType	full time, part time, contract, temporary, volunteer, internship
remoteFilter	on site, remote, hybrid
salary	Minimum: 40000, 60000, 80000, 100000, 120000
experienceLevel	internship, entry level, associate, senior, director, executive
limit	Number of jobs (default 10; 25 in the REST API)
sortBy	recent or relevant
page	Page number, from 0
has_verification	Verified jobs only
under_10_applicants	Jobs with fewer than 10 applicants

The REST API accepts every option except salary, experienceLevel, has_verification and under_10_applicants.

Responses

A search returns:

{
  "success": true,
  "total_jobs": 1,
  "jobs": [
    {
      "position": "Data Engineer (m/w/d)",
      "company": "Example GmbH",
      "location": "Berlin, Berlin, Germany",
      "date": "2026-10-20",
      "agoTime": "4 days ago",
      "salary": "Not specified",
      "jobUrl": "https://www.linkedin.com/jobs/view/1234567890/",
      "companyLogo": "https://media.licdn.com/…"
    }
  ],
  "search_params": { "keyword": "data engineer", "location": "Berlin, Germany", "limit": "10" }
}

Job details return:

{
  "id": "1234567890",
  "position": "Data Engineer (m/w/d)",
  "company": "Example GmbH",
  "location": "Berlin, Berlin, Germany",
  "postedAgo": "3 weeks ago",
  "applicants": "Over 200 applicants",
  "salary": null,
  "seniority": "Mid-Senior level",
  "employmentType": "Full-time",
  "jobFunction": null,
  "industries": "Software Development",
  "criteria": { "Seniority level": "Mid-Senior level", "Employment type": "Full-time", "Industries": "Software Development" },
  "acceptingApplications": true,
  "description": "We are looking for a data engineer to join our platform team. …",
  "jobUrl": "https://www.linkedin.com/jobs/view/1234567890/"
}

A job LinkedIn no longer has returns { "id": "…", "removed": true }.

REST API

npm run api or linkedin-jobs-mcp serve starts it on http://127.0.0.1:3000.

Endpoint	What it does
POST /search	Search, with the options above as a JSON body; returns { "cached", "total", "jobs" }
GET /job/:id, GET /job?url=<link>	One job's details; 404 when LinkedIn no longer has it, 400 for an invalid id or link
POST /mcp	MCP over Streamable HTTP
GET /health	{ "ok": true }

Library

const { query, getJobDetails } = require("linkedin-jobs-mcp");

const jobs = await query({ keyword: "data engineer", location: "Berlin, Germany", limit: "10" });
const job = await getJobDetails("https://www.linkedin.com/jobs/view/1234567890/");

Configuration

Set these in the environment or in a .env file in the folder you start it from.

Variable	Default	What it sets
PORT	3000	Port of the REST API and MCP over HTTP
HOST	127.0.0.1	Address the server listens on
LINKEDIN_REQUEST_TIMEOUT_MS	15000	Timeout of one request to LinkedIn
LINKEDIN_CACHE_TTL_MS	3600000	How long search results stay cached
LINKEDIN_DETAIL_MIN_INTERVAL_MS	1500	Minimum gap between two job-page requests
LINKEDIN_DETAIL_CACHE_TTL_MS	21600000	How long job details stay cached
LINKEDIN_USER_AGENT		User agent for job-page requests
API_RATE_LIMIT_WINDOW_MS	60000	Rate-limit window of the REST API
API_RATE_LIMIT_MAX	60	Requests per window and address

Build the Windows app

On Windows with Node.js 20 or newer:

npm install
npm run build:win     # dist/linkedin-jobs-mcp.exe and its SHA-256

The build bundles the code with esbuild and injects it into a copy of node.exe (a Node.js single executable application). Pushing a tag such as v1.2.0 builds the app on GitHub Actions and attaches it to a release.

Tests

npm test                              # job pages, settings files, the MCP server and the HTTP API
cd radar && python -m pytest          # Ghost Job Radar

Both run offline.

Repository layout

Path	What it is
index.js	LinkedIn search and job details
server.js, api.js	MCP server (stdio and HTTP) and REST API
app.js, clients.js, radar.js	The app: commands, client setup, starting the radar
radar/	Ghost Job Radar (Python): dashboard, scoring, research report and notes, browser extension
scripts/build-win.js	Builds the Windows app
test/	Tests of the Node part

Responsible use

The tools read public job pages slowly and cache what they read, for personal job searching and research. Automated access can conflict with LinkedIn's User Agreement, so keep the volume low and use it at your own risk.

License

PolyForm Noncommercial 1.0.0: free for personal, research and other non-commercial use. For commercial use, ask for permission.

Srinjoy Ghosh (@GhoshSrinjoy, ghoshsrinjoy97@gmail.com)