Manual setup required. The maintainer's config contains paths only you know - edit the placeholders below before adding it to Claude Code.
1
Prepare the server locally
Run this once before adding it to Claude Code.
git clone https://github.com/itsyashvardhan/mcp-server
cd mcp-server
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt2
Register it in Claude Code
claude mcp add job-listings-mcp -- python /path/to/main.pyReplace any placeholder paths in the command with the real path on your machine.
3
Make your agent remember this setup
job-listings-mcp's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.
npx conare@latestFree · one command · indexes the sessions already on disk. Set up in the browser instead →
What it does
- Multi-site job scraping using Jobspy
- Automated background scraping with APScheduler
- Smart deduplication of job listings
- REST API endpoints for filtering and querying jobs
- CORS-enabled for direct portfolio site integration
Tools 4
get_jobsRetrieve a list of job listings with optional filtering by location, keyword, and time.trigger_scrapeManually trigger a background job scraping process.get_statusCheck the status of the last scrape operation.get_rolesList all configured role tiers for job searching.Environment Variables
DATA_DIRDirectory path for persisting the SQLite database.Try it
→Find me recent AI solutions engineer jobs in San Francisco posted in the last 24 hours.
→Trigger a manual scrape to update the job listings database.
→What is the current status of the last job scrape operation?
→List all the configured role tiers available for job searching.
Original README from itsyashvardhan/mcp-server
MCP Server
A standalone Python microservice that scrapes fresh job listings using Jobspy, stores them in SQLite with deduplication, and exposes a /jobs REST endpoint for embedding in a portfolio site as a live feed.
Features
- Multi-site scraping
- Tiered role search
- Smart deduplication
- APScheduler
- Query filtering
- CORS-enabled
- Deploy-ready
Architecture
APScheduler (1hr) → Jobspy Scraper → SQLite (deduped) ← FastAPI /jobs
↕
Portfolio Site (fetch)
Quick Start
1. Clone & Install
cd jobs-mcp-server
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
2. Configure
cp .env.example .env
# Edit .env as needed
3. Run
python main.py
The server starts at http://localhost:8000. An initial scrape runs automatically in the background.
API Endpoints
`GET /` — Health Check
{
"status": "healthy",
"service": "Job Listings MCP Server",
"total_jobs_in_db": 142,
"scrape_interval_hours": 1
}
`GET /jobs` — List Job Listings
Query Params:
| Param | Type | Description |
|---|---|---|
location |
string | Filter by location (substring, case-insensitive) |
keyword |
string | Filter by keyword in job title |
hours |
int | Only jobs scraped within the last N hours |
limit |
int | Max results (default 100, max 500) |
offset |
int | Pagination offset |
Example:
curl "http://localhost:8000/jobs?location=San%20Francisco&keyword=AI&hours=24"
Response:
{
"count": 5,
"filters": {
"location": "San Francisco",
"keyword": "AI",
"hours": 24
},
"jobs": [
{
"id": 1,
"job_title": "AI Solutions Engineer",
"company": "Acme Corp",
"location": "San Francisco, CA",
"salary": "USD 120,000–160,000/yearly",
"apply_link": "https://linkedin.com/jobs/...",
"date_posted": "2025-01-15",
"date_scraped": "2025-01-15T12:00:00+00:00",
"source_site": "linkedin",
"role_tier": "T2 — Secondary"
}
]
}
`POST /scrape` — Manual Trigger
Triggers a scrape run in the background.
curl -X POST http://localhost:8000/scrape
`GET /status` — Last Scrape Status
curl http://localhost:8000/status
`GET /roles` — Configured Role Tiers
curl http://localhost:8000/roles
Deployment
Railway
- Fork the
mcp-serverrepo to a new GitHub repo (or subdirectory). - Connect Railway to the repo.
- Railway auto-detects the Dockerfile.
- Add a Volume at
/datato persist the SQLite DB. - Set environment variables in the Railway dashboard.
Render
- Create a new Web Service.
- Point to the repo/directory.
- Set Build Command:
pip install -r requirements.txt - Set Start Command:
python main.py - Add a Disk at
/dataand setDATA_DIR=/data.
🔗 Portfolio Integration
In your Next.js portfolio, fetch from the deployed URL:
// In a Next.js API route or client component
const API_URL = process.env.NEXT_PUBLIC_JOBS_API_URL || 'https://your-jobs-server.up.railway.app';
async function fetchJobs(filters?: { location?: string; keyword?: string; hours?: number }) {
const params = new URLSearchParams();
if (filters?.location) params.set('location', filters.location);
if (filters?.keyword) params.set('keyword', filters.keyword);
if (filters?.hours) params.set('hours', String(filters.hours));
const res = await fetch(`${API_URL}/jobs?${params.toString()}`);
return res.json();
}
License
MIT