What is Mcp Server Workshop

mcp-server-workshop is a lightweight, local-first code review tool that utilizes AI to provide quick feedback on code changes. It is built with TypeScript and powered by Ollama, analyzing git diffs and offering actionable suggestions to improve code quality.

Use cases

Use cases for mcp-server-workshop include providing quick feedback on personal projects, aiding in learning and experimentation, and serving as a lightweight tool for individual developers who need immediate code review without relying on cloud services.

How to use

To use mcp-server-workshop, first clone the repository and install the necessary dependencies. Then, pull the required AI model using Ollama. Make changes in your project and run the review tool with the command ‘npx tsx src/index.ts /path/to/your/project’ to receive feedback in your terminal.

Key features

Key features include local-first operation without the need for API keys or internet, automatic git integration for uncommitted changes, AI-powered intelligent code review using the deepseek-coder model, clear and actionable terminal output, fast setup, and privacy as the code never leaves your machine.

Where to use

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Local AI Code Reviewer 🔍

A lightweight, local-first code review tool that uses AI to provide quick feedback on your code changes. Built with TypeScript and powered by Ollama, it analyzes git diffs and offers actionable suggestions to improve your code.

Features ✨

• 🚀 Local-First: Runs entirely on your machine - no API keys or internet required
• 📝 Git Integration: Automatically analyzes your uncommitted changes
• 🤖 AI-Powered: Uses the deepseek-coder model for intelligent code review
• 🎨 Rich Output: Clear, colorized terminal output with actionable suggestions
• ⚡ Fast: Quick setup and instant feedback
• 🔒 Private: Your code never leaves your machine

Prerequisites 📋

Before you begin, ensure you have:

• Windows 11
• Node.js v22.0.0 or higher
• npm v11.0.0 or higher
• Ollama installed

Installation 🛠️

1. Clone the repository:

   git clone <repository-url>
   cd code-reviewer
   

2. Install dependencies:

   npm install
   

3. Pull the required model:

   ollama pull deepseek-coder:latest
   

4. Verify the installation:

   ollama list   # Should show deepseek-coder
   

Usage 💻

1. Make some changes in your project

2. Run the review tool:

   npx tsx src/index.ts /path/to/your/project
   

3. Review the suggestions in your terminal

Example output:

🔍 Code Review Assistant
─────────────────────
Reviewing project: /path/to/your/project

📋 Summary
Found 3 issues in your changes...

🎯 Detailed Analysis
[app.ts]
❌ ERROR (line 15)
   Unused variable 'config'
   → Impact: Increases code size and reduces maintainability
   → Fix: Remove unused variable or implement intended usage

...

Project Structure 🏗️

src/
  ├── index.ts      # CLI entry point
  ├── reviewer.ts   # Core review logic
  ├── git.ts        # Git operations
  ├── ollama.ts     # Model interface
  └── types.ts      # TypeScript types

Configuration 🔧

You can customize the review process by adding a .reviewconfig folder in your project:

your-project/
  ├── .reviewconfig/
  │   ├── standards.md    # Project coding standards
  │   └── patterns.md     # Preferred patterns
  └── ...

Troubleshooting 🔍

Common issues and solutions:

1. Ollama not responding

   • Check if Ollama is running in your system tray
   • Verify with ollama list
   • Restart Ollama if needed

2. Model issues

   • Ensure deepseek-coder is installed: ollama pull deepseek-coder:latest
   • Check available memory (8GB minimum recommended)
   • Close unnecessary applications

3. Node version errors

   • Update Node.js to v22+: node -v
   • Update npm to v11+: npm -v

Limitations 🚧

While powerful for quick code reviews, be aware that:

• Local models have limited capabilities compared to cloud alternatives
• Resource usage depends on your hardware
• Complex analyses might require cloud-based solutions
• Model responses can vary in quality

Best Practices 🌟

1. Use for:

   • Quick feedback on code changes
   • Personal projects
   • Learning and experimentation

2. Consider cloud alternatives for:

   • Team-wide code review
   • Production deployment
   • Complex analysis needs

License 📄

MIT License