What is Resume Updater

resume_updater is an AI-powered tool that utilizes the Model Context Protocol (MCP) agent framework to automatically tailor resumes based on specific job descriptions.

Use cases

Use cases for resume_updater include job seekers looking to optimize their resumes for specific positions, career coaches assisting clients in resume preparation, and recruiters who need to evaluate candidate resumes against job requirements.

How to use

To use resume_updater, input your current resume in PDF format, specify relevant GitHub repositories, and provide a job posting URL. The tool will analyze these inputs to generate a customized resume highlighting your skills and experiences pertinent to the job.

Key features

Key features of resume_updater include enhanced web scraping capabilities, an interactive user interface, HTML-based resume generation, page length optimization, and the use of isolated agents for specific tasks.

Where to use

resume_updater can be used in various fields such as job applications, career services, and recruitment processes, where tailored resumes are essential for improving job prospects.

resume_updater

An AI-powered tool that automatically tailors your resume for specific job applications using the Model Context Protocol (MCP) agent framework.

Project Overview

This tool analyzes your current resume, GitHub repositories, and a job posting to generate a tailored resume that highlights relevant skills and experiences for the target position.

Future

1. Enhanced Web Scraping

   • Implement advanced web scraping capabilities to handle JavaScript-rendered content and iframes
   • Explore integration with tools like Selenium or Playwright for dynamic content retrieval

2. Interactive User Interface

   • Develop a Streamlit-based chat interface for a more intuitive user experience
   • Provide GUI-based configuration of input.json as an alternative to manual editing
   • Enable real-time resume previews and adjustments

3. HTML-Based Resume Generation

   • Pivot from Markdown to HTML for greater control over formatting
   • Implement custom CSS styling for precise layout management
   • Maintain clean, semantic markup for accessibility

4. Page Length Optimization

   • Integrate PDF page length validation into the evaluator-optimizer workflow
   • Add automated font size and margin adjustments while preserving readability

5. More Isolated Agents/Prompts

   • Break down current monolithic agents into focused, single-task components
     • Resume analyzer
     • Job requirements extractor
     • Content optimizer
     • Format validator
   • Store prompts in separate YAML files for easy testing and iteration
   • Implement basic unit tests with mock responses

Approach

1. Input Processing

   • Reads and parses existing resume from PDF
   • Analyzes specified GitHub repositories for project details
   • Extracts requirements from job posting URL and description file
     • Caveat: The fetch-mcp server retrieves only web content present in HTML. To handle more complex content, such as JavaScript-rendered iFrames, additional MCP servers must be utilized in the future.

2. Content Optimization

   • Aggregates information from all sources
   • Uses an evaluator-optimizer workflow to generate optimized content
   • Ensures output maintains professional formatting and fits one page

3. Quality Control

   • Evaluates resume for relevance to job requirements
   • Verifies clear demonstration of skills
   • Checks formatting and length constraints

4. Output Generation

   • Generates the final resume in markdown format
   • Pauses to allow the user to review and tweak the tailored resume

Technologies Used

• MCP Agent Framework: Core orchestration system for AI agents
• OpenAI: Powers the AI agents for content analysis and generation
• MCP Servers:
  • fetch-mcp: Handles URL content retrieval
  • markdownify-mcp: Converts between formats
  • filesystem-mcp: Manages file operations
  • markdown2pdf-mcp: Converts markdown to PDF format

Setup Instructions

Python Dependencies

1. Install Poetry (if not already installed):
   Follow the instructions from the official Poetry documentation.

2. Navigate to Your Project Directory:

   cd /home/bnoffke/repos/resume_updater
   

3. Install the Python Dependencies:

   poetry install
   

npm Dependencies

1. Ensure Node.js and npm are Installed:
   You can download and install them from Node.js official website.

2. Navigate to Your Project Directory:

   cd /home/bnoffke/repos/resume_updater
   

3. Install the npm Dependencies:

   npm install
   

Install pnpm

1. Install pnpm (required for some dependencies):

   npm install -g pnpm
   

   Or follow alternative installation methods from the pnpm documentation.

Additional Requirements

1. Setup markdown2pdf-mcp server:

   • Clone the repository from markdown2pdf-mcp
   • Follow the installation instructions in the repository’s README

2. Setup markdownify-mcp server:

   • Clone the repository from markdownify-mcp
   • Install dependencies: pnpm install
   • Build the project: pnpm run build

3. Setup fetch-mcp server:

   • Clone the repository from fetch-mcp
   • Install dependencies: npm install
   • Build the project: npm run build

4. Configure MCP Servers
   Use the provided mcp_agent.config.yaml file in the repository as a reference and update the paths according to your local installations of the MCP servers.

Usage

1. Create or modify input.json with your configuration:

{
  "root_path": "/path/to/resume/directory",
  "resume_path": "current_resume.pdf",
  "github_repos": [
    "https://github.com/username/repo1",
    "https://github.com/username/repo2"
  ],
  "job_posting_url": "https://example.com/job-posting",
  "job_description_path": "job_description.txt",
  "output_path": "tailored_resume",
  "additional_info": "additional_info.txt"
}

2. Run the script with the config flag:

poetry run python resume_updater.py --config input.json

Configuration Parameters

• root_path: Base directory where your resume files are stored
• resume_path: Path to your current resume in PDF format (relative to root_path)
• github_repos: List of GitHub repository URLs that showcase relevant work
• job_posting_url: URL of the job posting to analyze
• job_description_path: Path to a text file containing additional job description details (relative to root_path)
• output_path: Base name for output files (will create both .md and .pdf, relative to root_path)
• additional_info: Optional path to a text file containing additional information to incorporate (relative to root_path)

The script will:

1. Generate a markdown file at {root_path}/{output_path}.md
2. Pause for your review and modifications
3. Create the final PDF at {root_path}/{output_path}.pdf using markdown2pdf-mcp server