Claude MCP Job Assistant

<p align="center"> <img src="https://raw.githubusercontent.com/eric623/Claude-MCP-Job-Assistant/main/job-flow.svg" alt="Claude MCP Job Assistant Workflow" width="1000"/> <br> <em>Figure 1 - MCP Job Assistant architecture illustrating the interaction between Claude Desktop, MCP Server, Tools, Resources, and Prompts.</em> </p>

Overview

Claude MCP Job Assistant is a production-ready MCP (Model Context Protocol) server designed to demonstrate how Tools, Resources, and Prompts can be orchestrated to build an intelligent job search assistant.

The project uses Claude Desktop as the MCP Host/Client and exposes a complete ecosystem for:

• Searching job opportunities.
• Saving interesting positions.
• Performing labor market analysis.
• Receiving personalized recommendations based on a resume.
• Generating matching reports between a resume and saved jobs.

This project provides a practical introduction to the Model Context Protocol and illustrates how modern AI assistants can leverage MCP capabilities to deliver contextual and personalized experiences.

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Features

• MCP-compliant server implementation.
• Integration with Claude Desktop.
• Intelligent job search.
• Job bookmarking and persistence.
• Resume-based recommendations.
• Labor market analysis.
• Matching reports generation.
• Modular architecture using MCP Tools, Resources, and Prompts.
• Production-ready setup with uv.

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Architecture

The MCP server is organized around the three core MCP primitives:

Tools

Tool	Description
search_jobs()	Retrieves job offers using an external API.
save_job()	Saves selected jobs in a structured format.

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Resources

Resource	Description
resume://default	Loads the user's resume.
jobs://saved	Loads previously saved jobs.

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Prompts

Prompt	Description
analyze_job_market()	Analyzes labor market trends.
personalized_job_recommender()	Suggests jobs, skills, and companies based on the user's resume.
create_match_report()	Generates a report comparing saved jobs with the user's resume.

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Workflow

User
 ↓
Claude Desktop (MCP Host)
 ↓
MCP Job Assistant Server
 ├── Tools
 │   ├── search_jobs()
 │   └── save_job()
 │
 ├── Resources
 │   ├── resume://default
 │   └── jobs://saved
 │
 └── Prompts
     ├── analyze_job_market()
     ├── personalized_job_recommender()
     └── create_match_report()

 ↓
Generated Response

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Technologies Used

• Python
• Model Context Protocol (MCP)
• Claude Desktop
• UV
• External Job APIs
• PDF Processing

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Installation

1. Clone the Repository

git clone https://github.com/eric623/Claude-MCP-Job-Assistant.git

cd Claude-MCP-Job-Assistant

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2. Install Dependencies

This project uses uv for dependency management.

uv sync

At this stage, the MCP server is ready to be launched by Claude Desktop.

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Claude Desktop Configuration

Step 1: Install Claude Desktop

Download and install Claude Desktop from Anthropic.

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Step 2: Open Developer Settings

Navigate to:

Settings → Developer → Edit Config

This opens the claude_desktop_config.json file.

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Step 3: Add the MCP Server

Add the following configuration:

{
  "mcpServers": {
    "mcp_job": {
      "command": "uv",
      "args": [
        "--directory",
        "PATH_TO_PROJECT_DIRECTORY",
        "run",
        "server.py"
      ]
    }
  }
}

Replace PATH_TO_PROJECT_DIRECTORY with the absolute path to your local project folder.

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Adding Your Resume

Place your CV inside the resume directory.

The file must be named exactly:

resume.pdf

Example:

Claude-MCP-Job-Assistant/
│
└── resume/
    └── resume.pdf

Important: Resume-based recommendations and matching reports require this file.

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Launching the MCP Server

After saving the configuration:

1. Completely close Claude Desktop.
2. Reopen Claude Desktop.

You should now see:

mcp_job (Running)

in the Developer panel.

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MCP Concepts Demonstrated

• MCP Tools
• MCP Resources
• MCP Prompts
• Claude Desktop Integration
• Context-Aware AI Systems
• Intelligent Job Search
• External API Consumption
• Personalized Recommendations
• Modular MCP Server Design

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Why This Project?

This project was built to explore and demonstrate the capabilities of the Model Context Protocol (MCP) by implementing a realistic use case centered around job exploration and career assistance.

It highlights how MCP enables:

• Context-aware assistants.
• Modular architectures.
• Tool orchestration.
• Resource management.
• Prompt engineering.
• Intelligent interactions between users and AI systems.

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Author

AKAKPO Koffi Moïse

• Interested in Agentic AI, Multi-Agent Systems, and Applied Artificial Intelligence.

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Claude MCP Job Assistant demonstrates how the Model Context Protocol can be leveraged to build intelligent, modular, and extensible assistants through the coordinated use of Tools, Resources, and Prompts.