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.

🚀 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)

Install the tool (see Installation section above)

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 .

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

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

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

📄 License

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

🙏 Acknowledgments

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

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
.github/workflows		.github/workflows
docs		docs
src		src
tests		tests
.analysis-config.json		.analysis-config.json
.gitignore		.gitignore
DISTRIBUTION.md		DISTRIBUTION.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
PACKAGE_STATUS.md		PACKAGE_STATUS.md
README.md		README.md
build_package.py		build_package.py
demo_enhancements.py		demo_enhancements.py
file1.py		file1.py
interactive_test.py		interactive_test.py
pyproject.toml		pyproject.toml
quick_test.py		quick_test.py
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
run_cli.py		run_cli.py
setup.py		setup.py
test_enhancements.py		test_enhancements.py
test_package.py		test_package.py

License

amritessh/mcp-code-analyzer

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MCP Code Analyzer

🚀 Features

Security Analysis

Code Quality

Complexity Analysis

Dependency Analysis

GitHub Integration

Enhanced Reporting & Visualization

CI/CD Integration

Trends & History Tracking

Multi-Language Support

📦 Installation

Option 1: Install from Source (Recommended for Development)

Option 2: Install as Python Package (Coming Soon)

Option 3: Docker (Coming Soon)

🚀 Getting Started

Quick Start (5 minutes)

Step-by-Step Guide

1. Initial Setup

2. Configure Your Project

3. Run Your First Analysis

4. Generate Reports

5. Visualize Dependencies

6. Set Up CI/CD Integration

7. Generate Status Badges

8. Track Trends and History

9. Analyze GitHub Repositories

🛠️ Usage

CLI Commands

CI/CD Integration

GitHub Actions

Quality Gates

Configuration

📊 Output Examples

Console Output

HTML Dashboard

Markdown Report

Dependency Graph (ASCII)

🔧 Advanced Features

Custom Analysis Rules

Export Options

Integration Examples

📚 Documentation

🤝 Contributing

📄 License

🙏 Acknowledgments

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