Which integrations are available for this server?

Uses OpenAI's GPT-4o-mini model to analyze resumes against job descriptions, identifying gaps, missing keywords, and providing structured recommendations for resume optimization

How do I use Resume Gap Analyzer?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Resume Gap Analyzer analyze my resume at /Users/alex/Documents/resume.pdf for this software engineer job" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

mcp_front

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 .docx files to the sample-resumes/ directory
Sample job descriptions are in sample-job-descriptions/
Run python test_cli.py for quick testing
In Claude Desktop, reference your resume files by absolute path

License

MIT

Resume Gap Analyzer