
JobSpy MCP Server
MCP server to search for jobs across multiple job listing platforms
What is JobSpy MCP Server?
JobSpy MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to search for jobs across multiple job listing platforms using the JobSpy tool.
How to use JobSpy MCP Server?
To use JobSpy MCP Server, clone the repository, install the dependencies, and start the server. You can then connect with AI assistants or web clients to perform job searches.
Key features of JobSpy MCP Server?
- Search for jobs across platforms like Indeed, LinkedIn, and Glassdoor.
- Filter job searches by terms, location, and time frames.
- Get structured job data in JSON or CSV format.
- Supports multiple transport options for integration with AI and web clients.
Use cases of JobSpy MCP Server?
- Finding job listings across various platforms for specific roles.
- Integrating with AI assistants to provide real-time job search capabilities.
- Enabling web applications to fetch job data dynamically.
FAQ from JobSpy MCP Server?
- Can JobSpy MCP Server search jobs on all platforms?
Yes! It can search across multiple job listing platforms like Indeed, LinkedIn, and Glassdoor.
- Is there a specific environment required to run JobSpy MCP Server?
Yes! You need Node.js 16+ and Python 3.6+ along with the JobSpy tool installed.
- How can I format the job search results?
You can format the results as JSON or CSV based on your requirements.
JobSpy MCP 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/yourusername/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 searchsearch_term
: Term to search forlocation
: 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