JobSpy Server

A Model Context Protocol (MCP) server that enables AI assistants like Claude to search for jobs across multiple job listing platforms using the JobSpy tool.

Features

• Search for jobs across multiple platforms (Indeed, LinkedIn, Glassdoor, etc.)
• Filter by search terms, location, time frames, and more
• Get structured job data that AI models can easily process
• Format results as JSON or CSV
• Multiple transport options: stdio for Claude integration, SSE for web clients

Prerequisites

• Node.js 16+
• Python 3.6+
• The JobSpy tool installed and available

Installation

# Clone the repository
git clone https://github.com/borgius/jobspy-mcp-server.git
cd jobspy-mcp-server

# Install dependencies
npm install

# Make sure the JobSpy tool is properly set up
cd ../jobSpy
pip install -r requirements.txt
chmod +x run.sh

Configuration

The server will automatically try to locate the JobSpy script in standard locations:

• ../jobSpy/run.sh (relative to the server directory)
• ./run.sh (in the current directory)
• /app/run.sh (for Docker environments)

Environment Variables

You can configure the server using the following environment variables:

Environment Variable	Description	Default
JOBSPY_DOCKER_IMAGE	Docker image to use for JobSpy	jobspy
JOBSPY_ACCESS_TOKEN	Access token for JobSpy API (if required)	none
PORT	Port for the MCP server	9423
HOST	Host for HTTP server	'0.0.0.0'
ENABLE_SSE	Enable Server-Sent Events transport	0

Setting Up Configuration

You can set these configuration values in multiple ways:

1. Using environment variables directly

export JOBSPY_DOCKER_IMAGE=jobspy
export JOBSPY_HOST='0.0.0.0'
export JOBSPY_PORT=9423
export ENABLE_SSE=1

2. Using a .env file

Create a .env file in the root directory with your configuration:

JOBSPY_DOCKER_IMAGE=jobspy
JOBSPY_HOST='0.0.0.0'
JOBSPY_PORT=9423
ENABLE_SSE=1

Usage

Starting the server

npm start

Connecting with Claude Desktop

Add the following to your Claude Desktop config file (typically at ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "jobspy": {
      "command": "node",
      "args": ["/path/to/jobspy-mcp-server/src/index.js"],
      "env": {
        "ENABLE_SSE": 0
      }
    }
  }
}

Using with Web Clients (SSE Transport)

The server exposes HTTP endpoints that allow web applications to interact with the JobSpy MCP server:

• Connect for updates: GET /mcp/connect

  • Establishes a Server-Sent Events (SSE) connection for real-time updates
  • Returns progress updates and job search results

• Send requests: POST /mcp/request

  • Accepts tool invocation requests in MCP format
  • Returns tool responses

Example JavaScript client for browser:

// Connect to SSE endpoint
const eventSource = new EventSource('http://localhost:9423/mcp/connect');

// Listen for updates
eventSource.onmessage = function(event) {
  const data = JSON.parse(event.data);
  console.log('Received update:', data);
  
  // Handle progress updates
  if (data.type === 'progress') {
    updateProgressBar(data.progress);
  }
};

// Send a search request
async function searchJobs() {
  const response = await fetch('http://localhost:9423/mcp/request', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      tool: 'search_jobs',
      params: {
        search_term: 'software engineer',
        location: 'San Francisco, CA',
        site_names: 'indeed,linkedin'
      }
    })
  });
  
  return await response.json();
}

API Usage

The server exposes the following endpoints:

Search Jobs

GET /search

Query parameters:

• site_names: Comma-separated list of job sites to search
• search_term: Term to search for
• location: Job location
• And other JobSpy parameters as needed

Available Tools

search_jobs

Searches for jobs across various job listing websites.

Parameters:

Parameter	Type	Description	Default
site_names	string	Comma-separated list of job sites to search (indeed,linkedin,zip_recruiter,glassdoor,google,bayt,naukri)	"indeed"
search_term	string	Search term for jobs	"software engineer"
location	string	Location for job search	"San Francisco, CA"
google_search_term	string	Google specific search term	null
results_wanted	integer	Number of results wanted	20
hours_old	integer	How many hours old the jobs can be	72
country_indeed	string	Country for Indeed search	"USA"
linkedin_fetch_description	boolean	Whether to fetch LinkedIn job descriptions (slower)	false
format	string	Output format (json or csv)	"json"
output	string	Output filename without extension	"jobs"

Example usage with Claude:

I need to find senior software engineer jobs in Boston posted in the last 24 hours on both LinkedIn and Indeed.

Docker Support

A Dockerfile is provided to containerize the MCP server:

# Build the Docker image
docker build -t jobspy-mcp-server .

# Run the container
docker run -p 9423:9423 jobspy-mcp-server

Development

Running in development mode

npm run dev

Running tests

npm test

curl -X POST "http://localhost:9423/api" \
  -H "Content-Type: application/json" \
  -d '{
    "method": "search_jobs",
    "params": {
      "search_term": "software engineer",
      "location": "San Francisco, CA",
      "site_names": "indeed,linkedin",
      "results_wanted": 10,
      "format": "json"
    }
  }'

License

MIT