jobswithgpt MCP Server

What does “jobswithgpt” MCP Server do?

The “jobswithgpt” MCP (Model Context Protocol) Server connects AI assistants with external job data sources to enhance job search and discovery workflows. By acting as a bridge between large language models (LLMs) and real-time job postings, the server empowers developers and AI tools to perform dynamic job searches, retrieve up-to-date job listings, and filter results based on user queries. This enables tasks such as searching for specific tech roles in certain locations, aggregating job market data, or integrating job search capabilities directly into AI-powered applications and assistants. The “jobswithgpt” MCP Server streamlines the process of surfacing relevant job opportunities, making it a valuable asset for anyone building AI solutions that require access to external job data and listings.

List of Prompts

No specific prompt templates are mentioned in the repository or documentation.

List of Resources

No explicit resources are listed in the repository or documentation.

List of Tools

• search_jobs: Enables querying for jobs through the MCP server. This tool is exposed to LLM clients, allowing them to programmatically search for job postings (e.g., “find jobs for python devs in sf”) and retrieve structured job data from external job sources.

Use Cases of this MCP Server

• Job Search Integration: Developers can integrate real-time job search capabilities into AI assistants or chatbots, allowing end users to search for jobs by title, skill, or location directly within the conversation.
• Automated Job Market Analysis: AI workflows can automatically gather and analyze job postings data to surface trends in hiring for specific skills, locations, or roles.
• Personalized Job Recommendations: By connecting to user profiles or resumes, the MCP server can help AI agents recommend tailored job opportunities.
• Job Alerts and Notifications: Set up automated alerts for new job postings that match predefined search criteria, keeping users updated on relevant opportunities.
• Recruitment Workflow Automation: Integrate job search actions into larger recruiting pipelines, helping recruiters or HR teams quickly surface matching candidates and roles.

How to set it up

Windsurf

No setup instructions provided for Windsurf.

Claude

1. Install prerequisites: Ensure Python > 3.12 and uv are installed for your OS.
2. Install the MCP server:

   uv run mcp install server.py
   

3. Verify installation: Check logs for confirmation (e.g., “Added server ‘jobswithgpt_search’”).
4. Example query:
   Use in Claude with a query like “Find ML jobs in SF”.

JSON Configuration Example:
Not specified for Claude in the repo.

Cursor

No setup instructions provided for Cursor.

Cline

No setup instructions provided for Cline.

Securing API Keys
No information on securing API keys or usage of environment variables is provided in the repository.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "jobswithgpt": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “jobswithgpt” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server is quite minimal, with basic documentation and only a single exposed tool for job searching. There’s a clear use case for integrating job searches into AI workflows, but the lack of prompt templates, resources, configuration details, and information on security or advanced MCP features limits its utility. The codebase appears to be in an early or demo stage.

MCP Score: 3/10 — Due to limited functionality, documentation, and lack of advanced MCP features.

MCP Score