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Selvage

by selvage-labversion 0.4.1i★ –iRuns on your computer
Registry nameio.github.selvage-lab/selvage

Install

Overview

Description from the maker (original English)

An LLM-based code review MCP server with AST-powered smart context extraction

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "selvage": {
      "command": "uvx",
      "args": [
        "selvage"
      ]
    }
  }
}

Run this in your terminal

claude mcp add selvage -- uvx selvage

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "selvage": {
      "command": "uvx",
      "args": [
        "selvage"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "selvage": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "selvage"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account selvage-lab, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

  • More stars than 0% of the other 4906 servers in this category
  • Published to the registry more recently than 4% of the other 4906 servers in this category
  • Basic checks equal to or better than 100% of the other 4906 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Selvage safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.1) reached the registry 9 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

GitHub

by github

GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

Computer or cloud★ 34,245MITUpdated 4 days ago
Install

Desktop Commander

by wonderwhy-er

MCP server for terminal commands, file operations, and process management

Runs on your computer★ 10,225MITUpdated 16 days ago
Install

Cloudflare MCP

by cloudflare

Cloudflare MCP servers

Runs in the cloud★ 4,446Apache-2.0Updated 14 months ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Selvage: AI-Powered Code Review Automation Tool

🌐 한국어

A modern CLI tool that helps AI analyze Git diffs to improve code quality, find bugs, and identify security vulnerabilities.

🤖 AI Agents : Read our documentation at https://selvage.ai/llms.txt

▶ Watch Demo Video

Selvage: Code reviews with an edge!

No more waiting for reviews! AI instantly analyzes your code changes to provide quality improvements and bug prevention. With smart context analysis (AST-based) that's accurate and cost-effective, plus multi-turn processing for large codebases - seamlessly integrated with all Git workflows.

Table of Contents

  • ✨ Key Features
  • 🚀 Quick Start
  • 🎯 Practical Usage Guide
  • MCP Mode Usage
  • ⌨️ CLI Usage
  • 🌐 Smart Context Analysis and Supported AI Models
  • 🎯 Smart Context Analysis
  • Supported AI Models
  • 📄 Review Result Storage Format
  • 🔧 Troubleshooting
  • 🤝 Contributing
  • 📜 License
  • 📋 Change Log
  • 📞 Contact and Community

✨ Key Features

  • 🤖 Multiple AI Model Support: Leverage the latest LLM models including OpenAI GPT-5, Anthropic Claude Sonnet-4, Google Gemini, and more
  • 🔍 Git Workflow Integration: Support for analyzing staged, unstaged, and changes between specific commits/branches
  • 🎯 Optimized Context Analysis: Tree-sitter based AST analysis automatically extracts the smallest code blocks containing changed lines along with their dependency statements, providing contextually optimized information for each situation
  • 🔄 Automatic Multi-turn Processing: Automatic prompt splitting when context limits are exceeded, supporting stable large-scale code reviews (Large Context Mode now auto-triggers once total tokens exceed 200k, even without provider errors)
  • 🤖 MCP Mode Support: Register as MCP mode in Cursor, Claude Code, etc., and request code reviews through natural language like "Review current changes"
  • 🔌 Claude Code Plugin: Install via marketplace with a single command — includes dedicated /review skill and selvage-reviewer agent for seamless integration
  • 🧠 Agent-Delegated Review (`get_review_context`): Returns structured review context (diff + Smart Context + system prompt) so host agents (Claude Code, Cursor, Antigravity, etc.) can perform code reviews with their own LLM — no API key required
  • 📖 Open Source: Freely use and modify under Apache-2.0 License

🚀 Quick Start

Common Setup
1. Installation

Recommended Method (using uv)

# Install uv (run once)

curl -LsSf https://astral.sh/uv/install.sh | sh

Install Selvage

uv tool install selvage

Alternative Method (using pipx)

# Install pipx (macOS)

brew install pipx

Install Selvage

pipx install selvage

Traditional Method (pip)

# ⚠️ May cause externally-managed-environment error on some systems

pip install selvage

macOS/Linux users: If you encounter errors with pip install, please use the uv or pipx methods above.

2. API Key Setup

Get an API key from OpenRouter and set it up:

export OPENROUTER_API_KEY="your_openrouter_api_key_here"
MCP Mode Usage (Recommended)

Register as MCP mode in Cursor, Claude Code, etc., to request code reviews through natural language.

Cursor Integration

Register in Cursor's MCP configuration file (path may vary depending on user environment):

Common path: ~/.cursor/mcp.json

// Method 1: Using environment variables (if already set)

{ "mcpServers": { "selvage": { "command": "uvx", "args": ["selvage", "mcp"] } } }

// Method 2: Direct specification { "mcpServers": { "selvage": { "command": "uvx", "args": ["selvage", "mcp"], "env": { "OPENROUTER_API_KEY": "your_openrouter_api_key_here" } } } }

Claude Code Integration
Method A: Plugin via Marketplace (Recommended)

Install the Selvage plugin from the marketplace to get the dedicated /review skill and selvage-reviewer agent:

# Step 1: Add Selvage marketplace

/plugin marketplace add selvage-lab/selvage

Step 2: Install the plugin

/plugin install selvage@selvage-lab-selvage

After installation, use the /review skill directly:

/review                      # Review unstaged changes

/review staged # Review staged changes /review branch main # Review against main branch /review commit abc1234 # Review from specific commit

💡 No API key required! The plugin uses get_review_context to leverage Claude Code's own LLM for code review, so no external API key is needed.

Method B: MCP Server Registration
# Method 1: Using environment variables (if already set)

claude mcp add selvage -- uvx selvage mcp

Method 2: Direct specification

claude mcp add selvage -e OPENROUTER_API_KEY=your_openrouter_api_key_here -- uvx selvage mcp

Usage

After restarting your IDE, request reviews from your Coding Assistant:

Please review current changes using selvage mcp

Review changes between current branch and main branch using claude-sonnet-4-thinking with selvage mcp

🎉 Done! Selvage will analyze the code, review it, and deliver results through your Coding Assistant.

CLI Mode Usage

For direct terminal usage:

selvage review --model claude-sonnet-4-thinking

💡 More Options: CLI Usage | Practical Usage Guide

🎯 Practical Usage Guide

MCP Mode Usage
Basic Usage
# Basic review request

Please review current changes using selvage mcp

Review staged changes

Review staged work using gpt-5-high with selvage mcp

Review against specific branch

Review current branch against main branch using selvage mcp

Review with automatic model selection

Review current branch against main branch using selvage mcp, automatically selecting appropriate model

Agent-Delegated Review (No API Key Required)

The get_review_context tool returns structured review context so host agents can perform code reviews with their own LLM — no Selvage API key needed.

# Request agent-delegated review context

Get review context for current changes using selvage mcp, then review the code

Agent-delegated review for staged changes

Get review context for staged changes using selvage mcp

Agent-delegated review against branch

Get review context comparing current branch to main using selvage mcp

💡 How it works: Selvage extracts diff + AST-based Smart Context + system prompt and returns it as structured context. The host agent (Claude Code, Cursor, Antigravity, etc.) then performs the review directly with its own LLM, without needing an external API key.

Advanced Workflows

Multi-model Comparison Review

Review staged work using both gpt-5-high and claude-sonnet-4-thinking with selvage mcp, then compare the results

Stepwise Code Improvement Workflow

1. Review current changes using claude-sonnet-4-thinking with selvage mcp
  1. Critically evaluate review feedback for validity against current codebase and set priorities
  2. Apply improvements sequentially based on established priorities

CI/CD Integration Scenarios

# Code quality verification before PR creation

Review changes against main branch using selvage mcp for code quality verification before PR creation

Final check before deployment

Perform comprehensive review of staged changes using selvage mcp for final check before deployment

⌨️ CLI Usage

Direct terminal usage method. While MCP mode is recommended, CLI is useful for scripts and CI/CD.

Configuring Selvage
# View all settings

selvage config list

Set default model

selvage config model

Set default language

selvage config language

Code Review
selvage review [OPTIONS]
Key Options
  • --repo-path : Git repository path (default: current directory)
  • --staged: Review only staged changes
  • --target-commit : Review changes from specific commit to HEAD (e.g., abc1234)
  • --target-branch : Review changes between current branch and specified branch (e.g., main)
  • --model : AI model to use (e.g., claude-sonnet-4-thinking)
  • --open-ui: Automatically launch UI after review completion
  • --no-print: Don't output review results to terminal (terminal output enabled by default)
  • --skip-cache: Perform new review without using cache
Usage Examples
# Review current working directory changes

selvage review

Final check before commit

selvage review --staged

Review specific files only

git add specific_files.py && selvage review --staged

Code review before sending PR

selvage review --target-branch develop

Quick and economical review for simple changes

selvage review --model gemini-2.5-flash

Review and then view detailed results in web UI

selvage review --target-branch main --open-ui

Git Workflow Integration
Team Collaboration Scenarios
# Code quality verification before Pull Request creation

selvage review --target-branch main --model claude-sonnet-4-thinking

Pre-analysis of changes for code reviewers

selvage review --target-branch develop --model claude-sonnet-4-thinking

Comprehensive review of all changes after specific commit

selvage review --target-commit a1b2c3d --model claude-sonnet-4-thinking

Development Stage Quality Management
# Quick feedback during development (before WIP commit)

selvage review --model gemini-2.5-flash

Final verification of staged changes (before commit)

selvage review --staged --model claude-sonnet-4-thinking

Emergency review before hotfix deployment

selvage review --target-branch main --model claude-sonnet-4-thinking

Large-scale Code Review
# Large codebases are automatically handled

selvage review --model claude-sonnet-4 # Usage is the same, multi-turn processing automatically applied after detection

Selvage automatically handles large code changes that exceed LLM model context limits. Once usage reaches roughly 200k tokens (tiktoken basis), Large Context Mode starts automatically, so just wait for it to complete.

Cost Optimization
# Use economical models for small changes

selvage review --model gemini-2.5-flash

Viewing Results

Review results are output directly to the terminal and automatically saved to files simultaneously.

For additional review management and re-examination, you can use the web UI:

# Manage all saved review results in web UI

selvage view

R

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