mcp-code-analyzer

amritessh/mcp-code-analyzer

3.1

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MCP Code Analyzer is a comprehensive tool designed to enhance code security, quality, and maintainability through detailed analysis and visualization.

MCP Code Analyzer

A comprehensive code analysis tool that provides security scanning, quality checks, complexity analysis, and dependency visualization. Available as a Python MCP server, CLI tool, and VS Code extension.

Version Python License

🚀 Features

Security Analysis

  • 🔒 Vulnerability Detection: Identifies security vulnerabilities including SQL injection, XSS, hardcoded secrets
  • 🛡️ Dependency Scanning: Checks for known vulnerabilities in dependencies
  • 🔑 Secret Detection: Finds exposed API keys, passwords, and tokens
  • 📊 Risk Scoring: Provides overall security risk assessment

Code Quality

  • 📏 Quality Metrics: Measures code quality with actionable insights
  • 🧹 Dead Code Detection: Identifies unused imports, variables, and functions
  • 📝 TODO/FIXME Tracking: Manages technical debt and pending tasks
  • 🎯 Code Smell Detection: Identifies problematic patterns

Complexity Analysis

  • 🧮 Cyclomatic Complexity: Measures code complexity at function level
  • 📈 Maintainability Index: Calculates overall code maintainability
  • 🔥 Hotspot Detection: Identifies complex areas needing refactoring
  • 📊 Halstead Metrics: Provides detailed complexity measurements

Dependency Analysis

  • 🔗 Dependency Mapping: Visualizes module dependencies
  • 🔄 Circular Dependency Detection: Finds and suggests fixes for circular imports
  • 📦 External Dependency Tracking: Monitors third-party dependencies
  • 🏗️ Architecture Analysis: Identifies architectural patterns and violations

GitHub Integration

  • 🐙 Repository Analysis: Analyze any public GitHub repository
  • 🔍 Quick Scanning: Fast analysis without full clone
  • 📊 Repository Comparison: Compare multiple repositories
  • 🛡️ Security Scanning: Check for security advisories and vulnerabilities

Enhanced Reporting & Visualization

  • 📊 Interactive Dashboards: HTML dashboards with charts and metrics
  • 📈 ASCII/Markdown Visualizations: Dependency graphs and complexity heatmaps
  • 📋 Multiple Export Formats: HTML, Markdown, JSON, and JUnit XML
  • 🎨 Professional Reports: Executive summaries and detailed technical reports
  • 📊 Trend Analysis: Track code quality improvements over time

CI/CD Integration

  • 🔄 GitHub Actions: Automated analysis on pull requests and pushes
  • 🚦 Quality Gates: Configurable thresholds for build success/failure
  • 🏷️ Badge Generation: Status badges for README and documentation
  • 📊 PR Comments: Automatic analysis summaries on pull requests
  • 🔧 Multi-Platform Support: GitLab CI, Jenkins, Azure DevOps

Trends & History Tracking

  • 📜 Analysis History: Track all analysis runs with timestamps
  • 📈 Trending Issues: Identify recurring security and quality problems
  • 📊 Metrics Tracking: Monitor code quality improvements over time
  • 🔍 Historical Comparison: Compare current vs. previous analysis results

Multi-Language Support

  • 🐍 Python (full support)
  • 🟨 JavaScript/TypeScript (full support)
  • 📄 Generic support for other languages

📦 Installation

Option 1: Install from Source (Recommended for Development)

# Clone the repository
git clone https://github.com/yourusername/mcp-code-analyzer.git
cd mcp-code-analyzer

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .

Option 2: Install as Python Package (Coming Soon)

# Install directly from PyPI (when published)
pip install mcp-code-analyzer

# Or install from GitHub
pip install git+https://github.com/yourusername/mcp-code-analyzer.git

Option 3: Docker (Coming Soon)

# Pull and run with Docker
docker pull yourusername/mcp-code-analyzer
docker run -v $(pwd):/workspace mcp-code-analyzer analyze-project /workspace

🚀 Getting Started

Quick Start (5 minutes)

  1. Install the tool (see Installation section above)

  2. Analyze your first project:

    # Basic analysis with console output
python run_cli.py analyze-project /path/to/your/project

# Or analyze the current directory
python run_cli.py analyze-project .

  3. Generate your first report:

    # HTML report
python run_cli.py generate-report . --format html --type detailed

# Markdown report
python run_cli.py generate-report . --format markdown --type executive

  4. View dependency visualization:

    # ASCII dependency graph
python run_cli.py dependencies . --visualize --format text

# Export Mermaid diagram
python run_cli.py dependencies . --format mermaid --export-graph deps.mmd

Step-by-Step Guide

1. Initial Setup
# Clone and setup
git clone https://github.com/yourusername/mcp-code-analyzer.git
cd mcp-code-analyzer
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
2. Configure Your Project

Create a configuration file in your project root:

# Copy the sample configuration
cp .analysis-config.json /path/to/your/project/

# Edit the configuration for your needs
nano /path/to/your/project/.analysis-config.json

Example configuration:

{
  "analysis": {
    "include_patterns": ["src/**/*.py", "tests/**/*.py"],
    "exclude_patterns": ["**/__pycache__/**", "**/*.pyc"],
    "max_complexity": 15,
    "security_rules": ["all"]
  },
  "quality_gates": {
    "critical_issues": 0,
    "high_issues": 5,
    "overall_health_score": 80
  },
  "ci": {
    "fail_on_critical": true,
    "comment_on_pr": true
  }
}
3. Run Your First Analysis
# Navigate to your project
cd /path/to/your/project

# Run comprehensive analysis
python /path/to/mcp-code-analyzer/run_cli.py analyze-project . --format console

# This will show:
# - Security issues found
# - Code quality metrics
# - Complexity analysis
# - Dependency information
# - Overall health score
4. Generate Reports
# Generate detailed HTML report
python /path/to/mcp-code-analyzer/run_cli.py generate-report . \
  --format html \
  --type detailed \
  --output analysis_report.html

# Generate executive summary
python /path/to/mcp-code-analyzer/run_cli.py generate-report . \
  --format markdown \
  --type executive \
  --output executive_summary.md
5. Visualize Dependencies
# View ASCII dependency graph
python /path/to/mcp-code-analyzer/run_cli.py dependencies . \
  --visualize \
  --format text

# Export Mermaid diagram for documentation
python /path/to/mcp-code-analyzer/run_cli.py dependencies . \
  --format mermaid \
  --export-graph docs/dependency-graph.mmd

# Export Graphviz for advanced visualization
python /path/to/mcp-code-analyzer/run_cli.py dependencies . \
  --format graphviz \
  --export-graph docs/dependency-graph.dot
6. Set Up CI/CD Integration

Copy the GitHub Actions workflow to your repository:

# Create workflows directory
mkdir -p .github/workflows

# Copy the workflow file
cp /path/to/mcp-code-analyzer/.github/workflows/code-analysis.yml .github/workflows/

The workflow will automatically:

  • Run analysis on every PR and push
  • Generate reports and upload as artifacts
  • Comment on PRs with analysis summary
  • Fail builds if quality gates are not met
7. Generate Status Badges
# Generate badge for overall health
python /path/to/mcp-code-analyzer/run_cli.py generate-badge . \
  --metric overall_health \
  --threshold 80 \
  --output badge.md

# Generate badge for security score
python /path/to/mcp-code-analyzer/run_cli.py generate-badge . \
  --metric security_score \
  --threshold 90 \
  --output security_badge.md

Add the badges to your README:

![Code Quality](https://img.shields.io/badge/code%20quality-85%2F100-green)
![Security](https://img.shields.io/badge/security-92%2F100-green)
8. Track Trends and History
# View analysis history
python /path/to/mcp-code-analyzer/run_cli.py show-history

# View trending issues
python /path/to/mcp-code-analyzer/run_cli.py show-trends
9. Analyze GitHub Repositories
# Analyze any public repository
python /path/to/mcp-code-analyzer/run_cli.py analyze-github \
  https://github.com/python/cpython \
  --export markdown

# Full analysis with cloning (requires GitHub token)
python /path/to/mcp-code-analyzer/run_cli.py analyze-github \
  https://github.com/python/cpython \
  --token YOUR_GITHUB_TOKEN \
  --full \
  --export html

🛠️ Usage

CLI Commands

# Analyze a project
python run_cli.py analyze-project /path/to/project

# Analyze dependencies with visualization
python run_cli.py dependencies /path/to/project --visualize --format mermaid

# Analyze GitHub repository
python run_cli.py analyze-github https://github.com/user/repo --export markdown

# Generate comprehensive report
python run_cli.py generate-report /path/to/project --format html --type detailed

# Run quality gate checks
python run_cli.py quality-gate /path/to/project --min-health-score 85

# Generate status badge
python run_cli.py generate-badge /path/to/project --metric security_score --threshold 90

# Show analysis history
python run_cli.py show-history

# Show trending issues
python run_cli.py show-trends

CI/CD Integration

GitHub Actions
# .github/workflows/code-analysis.yml
name: Code Analysis
on: [push, pull_request]

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - uses: actions/setup-python@v4
      with:
        python-version: '3.9'
    - run: |
        pip install -r requirements.txt
        python run_cli.py analyze-project . --format markdown --output ./reports
    - uses: actions/upload-artifact@v3
      with:
        name: code-analysis-report
        path: ./reports/
Quality Gates
# Fail build if critical issues found
python run_cli.py quality-gate . --fail-on-critical --min-health-score 80

# Generate badges for documentation
python run_cli.py generate-badge . --metric overall_health --threshold 85 --output badge.md

Configuration

Create .analysis-config.json in your project root:

{
  "analysis": {
    "include_patterns": ["src/**/*.py", "tests/**/*.py"],
    "exclude_patterns": ["**/__pycache__/**", "**/*.pyc"],
    "max_complexity": 15,
    "security_rules": ["all"]
  },
  "quality_gates": {
    "critical_issues": 0,
    "high_issues": 5,
    "overall_health_score": 80
  },
  "ci": {
    "fail_on_critical": true,
    "comment_on_pr": true
  }
}

📊 Output Examples

Console Output

🔍 Code Analysis Results
========================
📊 Overview
  • Files Analyzed: 45
  • Total Issues: 12
  • Average Complexity: 8.2
  • Overall Health: 85/100

🔒 Security Issues (3)
  1. HIGH Hardcoded API key in config.py:15
  2. MEDIUM SQL injection risk in user.py:42
  3. LOW Missing input validation in api.py:78

✨ Quality Issues (5)
  1. Function too long (50+ lines) in utils.py:120
  2. Missing docstring in helper.py:15
  3. Unused import 'datetime' in main.py:3

HTML Dashboard

  • Interactive charts and metrics
  • Clickable file navigation
  • Severity-based color coding
  • Exportable visualizations

Markdown Report

  • Executive summary
  • Detailed technical analysis
  • Code snippets with line numbers
  • Fix suggestions and references

Dependency Graph (ASCII)

- src/
  - analyzers/
    - security.py
    - quality.py
    - dependencies.py
  - utils/
    - visualizer.py
    - logger.py
  - cli.py

🔧 Advanced Features

Custom Analysis Rules

{
  "custom_rules": {
    "max_function_length": 50,
    "require_docstrings": true,
    "naming_conventions": "snake_case"
  }
}

Export Options

# Export dependency graph
python run_cli.py dependencies . --export-graph deps.mmd --format mermaid

# Generate JUnit XML for test runners
python run_cli.py analyze-project . --format junit --output test-results.xml

# Create executive summary
python run_cli.py generate-report . --type executive --format markdown

Integration Examples

# Send results to external monitoring
python run_cli.py analyze-project . --format json | \
  curl -X POST https://api.monitoring.com/analysis \
  -H "Content-Type: application/json" -d @-

# Generate badges for multiple metrics
for metric in overall_health security_score quality_score; do
  python run_cli.py generate-badge . --metric $metric --threshold 80
done

📚 Documentation

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests
  5. Run the analysis: python run_cli.py analyze-project .
  6. Submit a pull request

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

  • Built with Rich for beautiful CLI output
  • Uses NetworkX for dependency analysis
  • Integrates with GitHub API for repository analysis