linkedin_mcp / app.py
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from __future__ import annotations
import os
import re
import time
import html
from typing import List, Optional
from urllib.parse import urlencode
import httpx
from pydantic import BaseModel, Field, HttpUrl
from fastmcp import FastMCP
mcp = FastMCP(
name="linkedin-jobs",
host="0.0.0.0",
port=7861,
)
class JobPosting(BaseModel):
title: str = Field(..., description="Job title")
company: Optional[str] = Field(None, description="Company name if available")
location: Optional[str] = Field(None, description="Job location if available")
url: HttpUrl = Field(..., description="Direct link to the LinkedIn job page")
job_id: Optional[str] = Field(None, description="LinkedIn job ID parsed from URL, if found")
listed_text: Optional[str] = Field(None, description="Human-readable posted time text, e.g., '3 days ago'")
def _default_headers(cookie: Optional[str]) -> dict:
headers = {
"User-Agent": (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/125.0.0.0 Safari/537.36"
),
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
"Accept-Language": "en-US,en;q=0.9",
"Cache-Control": "no-cache",
"Pragma": "no-cache",
"Connection": "keep-alive",
}
if cookie:
headers["Cookie"] = cookie
return headers
def _parse_jobs_from_html(html_text: str) -> list[JobPosting]:
try:
from selectolax.parser import HTMLParser
except Exception:
raise RuntimeError(
"selectolax is required. Ensure it is listed in requirements.txt and installed."
)
tree = HTMLParser(html_text)
jobs: list[JobPosting] = []
# LinkedIn search uses job cards with these classes
for card in tree.css(".base-search-card, .job-search-card"):
link_el = card.css_first("a.base-card__full-link, a.hidden-nested-link, a")
title_el = card.css_first("h3.base-search-card__title, .base-search-card__title, .sr-only")
company_el = card.css_first(
"h4.base-search-card__subtitle, .base-search-card__subtitle, .job-search-card__subtitle, .hidden-nested-link+div"
)
location_el = card.css_first(".job-search-card__location, .base-search-card__metadata > .job-search-card__location")
time_el = card.css_first("time, .job-search-card__listdate, .job-search-card__listdate--new")
url = (link_el.attributes.get("href") if link_el else None) or ""
title = (title_el.text(strip=True) if title_el else "").strip()
company = (company_el.text(strip=True) if company_el else None)
location = (location_el.text(strip=True) if location_el else None)
listed_text = (time_el.text(strip=True) if time_el else None)
if not url or not title:
continue
# Clean up HTML entities and whitespace
title = html.unescape(re.sub(r"\s+", " ", title))
if company:
company = html.unescape(re.sub(r"\s+", " ", company))
if location:
location = html.unescape(re.sub(r"\s+", " ", location))
if listed_text:
listed_text = html.unescape(re.sub(r"\s+", " ", listed_text))
# Derive job id from URL if present: /jobs/view/<id>/
job_id_match = re.search(r"/jobs/view/(\d+)", url)
job_id = job_id_match.group(1) if job_id_match else None
try:
jobs.append(
JobPosting(
title=title,
company=company,
location=location,
url=url, # type: ignore[arg-type]
job_id=job_id,
listed_text=listed_text,
)
)
except Exception:
# Skip malformed entries gracefully
continue
return jobs
def _search_page(client: httpx.Client, query: str, location: Optional[str], start: int) -> list[JobPosting]:
params = {
"keywords": query,
"start": start,
}
if location:
params["location"] = location
# First request the main search page (richer HTML for the first 25 results)
url = "https://www.linkedin.com/jobs/search/?" + urlencode(params)
resp = client.get(url, follow_redirects=True, timeout=20.0)
resp.raise_for_status()
jobs = _parse_jobs_from_html(resp.text)
# For subsequent starts (>0), LinkedIn often uses this fragment endpoint
if start > 0 and len(jobs) == 0:
fragment_url = (
"https://www.linkedin.com/jobs-guest/jobs/api/seeMoreJobPostings/search?" + urlencode(params)
)
frag_resp = client.get(fragment_url, follow_redirects=True, timeout=20.0)
if frag_resp.status_code == 200:
jobs = _parse_jobs_from_html(frag_resp.text)
return jobs
@mcp.tool(description="Search LinkedIn job listings and return structured job postings. Optionally set LINKEDIN_COOKIE env for authenticated scraping.")
def search_linkedin_jobs(query: str, location: Optional[str] = None, limit: int = 25, pages: int = 1) -> List[JobPosting]:
"""
- query: Search keywords, e.g. "machine learning engineer"
- location: Optional location filter, e.g. "Paris, Île-de-France, France"
- limit: Maximum number of jobs to return (<= 200)
- pages: Number of pages to fetch (each page is ~25 results)
Note: LinkedIn may throttle or require authentication. You can set the environment
variable LINKEDIN_COOKIE to a valid cookie string (e.g., including li_at) for better results.
"""
cookie = os.environ.get("LINKEDIN_COOKIE")
max_items = max(1, min(limit, 200))
pages = max(1, min(pages, 8))
headers = _default_headers(cookie)
all_jobs: list[JobPosting] = []
with httpx.Client(headers=headers) as client:
start = 0
for page in range(pages):
try:
jobs = _search_page(client, query=query, location=location, start=start)
except httpx.HTTPStatusError as e:
# If unauthorized or blocked, break early
status = e.response.status_code
if status in (401, 403, 429):
break
raise
except Exception:
# transient errors: move to next page
jobs = []
if not jobs:
# If no jobs were parsed, stop to avoid hammering
break
all_jobs.extend(jobs)
if len(all_jobs) >= max_items:
break
start += 25
# Be polite to avoid rate-limiting
time.sleep(0.8)
return all_jobs[:max_items]
if __name__ == "__main__":
mcp.run(transport="http")