Resume Gap Analyzer
Analyzes resumes against job descriptions to identify missing skills, keywords, and improvement opportunities using AI. Provides structured feedback including gap analysis, ATS optimization suggestions, and actionable recommendations to improve job application success.
README
Resume Gap Analyzer MCP Server
An MCP (Model Context Protocol) server that analyzes resumes against job descriptions to identify gaps, missing keywords, and improvement opportunities using OpenAI's GPT-4o-mini.
https://www.youtube.com/watch?v=JnteMtI5nCs
Features
- Gap Analysis: Identifies missing skills, experience, certifications, and keywords
 - Prioritized Recommendations: Actionable feedback organized by impact
 - ATS Optimization: Ensures resume compatibility with Applicant Tracking Systems
 - Strength Mapping: Highlights existing qualifications to emphasize
 - Sample Resume Resources: Test with pre-loaded sample resumes
 
Setup
Prerequisites
- Python 3.10+
 - OpenAI API key (Get one here)
 
Installation
- Clone the repository:
 
git clone <repository-url>
cd gapsinmyresume
- Create and activate a virtual environment:
 
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
- Install dependencies:
 
pip install -r requirements.txt
- Set your OpenAI API key:
 
# Copy the example env file
cp .env.example .env
# Edit .env and add your actual API key
# OPENAI_API_KEY=sk-your-actual-key-here
Configuration for Claude Desktop
To use this MCP server with Claude Desktop, add it to your config file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "resume-gap-analyzer": {
      "command": "/absolute/path/to/gapsinmyresume/.venv/bin/python",
      "args": ["/absolute/path/to/gapsinmyresume/server.py"]
    }
  }
}
Important: Replace /absolute/path/to/gapsinmyresume with your actual path. Use the .venv/bin/python from the virtual environment you created.
After updating the config, restart Claude Desktop for changes to take effect.
Usage
Option 1: Via Claude Desktop (Recommended)
Once configured, simply ask Claude in the desktop app:
"Analyze my resume at /Users/you/Documents/resume.docx against this job description:
[paste full job description here]"
Claude will automatically use the analyze_resume_gaps tool.
Option 2: Direct CLI Testing
Run the included test script to test without Claude Desktop:
# Make sure your .env file has OPENAI_API_KEY set
source .venv/bin/activate
python test_cli.py
This will analyze the first resume in sample-resumes/ against a sample job description.
Option 3: MCP Server Directly
Start the MCP server (for debugging or integration):
source .venv/bin/activate
python server.py
The server runs in stdio mode and accepts MCP protocol messages.
Tool Parameters
analyze_resume_gaps
resume_path(string, required): Absolute path to resume file (.docx)job_description(string, required): Full text of the job posting
Example in Claude Desktop:
"Analyze /Users/you/Documents/resume.docx for the Senior Software Engineer role at XYZ Corp.
Job description:
We are seeking a Senior Software Engineer with 5+ years of experience in Python, AWS, and React..."
Output Format
The tool returns structured JSON with:
{
  "overall_match_score": 75,
  "critical_gaps": [
    {
      "category": "hard_skill",
      "gap": "AWS certification missing",
      "importance": "high",
      "recommendation": "Obtain AWS Solutions Architect certification",
      "keywords_to_add": ["AWS", "cloud architecture", "EC2"]
    }
  ],
  "missing_keywords": {
    "technical": ["Kubernetes", "Docker", "CI/CD"],
    "soft_skills": ["leadership", "mentoring"],
    "industry_terms": ["agile", "scrum"]
  },
  "experience_analysis": {
    "required_years": 5,
    "resume_shows_years": 3,
    "gap_exists": true,
    "notes": "Resume shows 3 years but requires 5+"
  },
  "strengths_to_highlight": [
    {
      "strength": "Python expertise",
      "relevance": "Primary language for the role",
      "current_prominence": "medium",
      "recommendation": "Move Python projects to top of experience section"
    }
  ],
  "formatting_suggestions": [
    {
      "issue": "No clear metrics in achievements",
      "impact": "both",
      "fix": "Add quantifiable results (e.g., 'Improved performance by 40%')"
    }
  ],
  "content_improvements": [
    {
      "section": "Professional Summary",
      "current_state": "Generic software engineer summary",
      "suggested_change": "Emphasize Python, AWS, and senior-level leadership",
      "example": "Senior Software Engineer with 3+ years specializing in Python-based cloud solutions...",
      "priority": "high"
    }
  ],
  "quick_wins": [
    "Add 'AWS' keyword to technical skills section",
    "Quantify achievement in Project X with metrics",
    "Reorder skills to match job description priority"
  ],
  "summary": "Strong Python foundation but needs more AWS experience. Highlight existing cloud projects and add relevant certifications. Focus on quantifying achievements."
}
Development
# Run the server directly (for testing)
python server.py
# Format code
black server.py prompts.py
# Lint code
ruff check .
File Structure
gapsinmyresume/
├── server.py                           # Main FastMCP server implementation
├── prompts.py                          # System prompts and Pydantic models
├── test_cli.py                         # CLI test script
├── sample-resumes/                     # Place sample .docx files here
├── sample-job-descriptions/            # Example job descriptions
│   └── senior-software-engineer.txt
├── pyproject.toml                      # Python project configuration
├── requirements.txt                    # Python dependencies
├── .env.example                        # Environment variables template
├── .env                                # Your API keys (git ignored)
├── README.md                           # This file
└── SETUP.md                            # Additional setup notes
How It Works
- Document Parsing: Extracts text from DOCX files using python-docx
 - LLM Analysis: Sends resume and job description to OpenAI gpt-4o-mini
 - Structured Output: Uses Pydantic models with OpenAI's structured output API
 - MCP Integration: FastMCP exposes analysis as a tool callable from Claude Desktop
 
Tips for Best Results
- Provide complete job descriptions (not just titles)
 - Ensure resume files are properly formatted .docx files
 - Include both required and preferred qualifications in job description
 - Use the quick wins section for immediate, high-impact changes
 - Place test resumes in 
sample-resumes/to use the resource feature 
Quick Start Example
After setup, try this:
# 1. Activate virtual environment
source .venv/bin/activate
# 2. Run the test script (uses sample resume + sample job description)
python test_cli.py
# 3. Or use with Claude Desktop
# Just ask: "Analyze my resume at /path/to/resume.docx against this job description: [paste description]"
Testing with Sample Resumes
- Add 
.docxfiles to thesample-resumes/directory - Sample job descriptions are in 
sample-job-descriptions/ - Run 
python test_cli.pyfor quick testing - In Claude Desktop, reference your resume files by absolute path
 
License
MIT
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