Uv

If your version is older than 0.9.0, upgrade for the best experience:

# Using pip
pip install --upgrade uv

# Or reinstall using official installer
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

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

See Recent Changes Reference for detailed version information and migration guidance.

When to Use This Skill

Use this skill when:

• Setting up Python virtual environments and managing Python versions
• Installing and managing Python CLI tools (development tools, utilities)
• Running MCP servers with UVX
• Deciding between uv tool install vs uvx for package execution
• Configuring VS Code or other IDEs for MCP server integration
• Migrating from pip, pipx, or poetry to UV
• Troubleshooting UV-related issues

Skip this skill when:

• You need basic Python package installation only (standard pip documentation may suffice)
• Working with legacy Python 2.x projects

Core Concepts

1. UV Commands Overview

UV provides several commands for different use cases:

2. Tool vs UVX Decision Tree

Need to run a Python package?
|
├─ Use daily/frequently?
|  └─ YES → `uv tool install package`
|     Examples: black, pytest, flake8, mypy
|
├─ MCP server?
|  └─ YES → `uvx package` or `uvx --from path script.py`
|     Examples: mcp-server-sqlite, custom MCP servers
|
├─ Testing/one-off execution?
|  └─ YES → `uvx package`
|     Examples: testing new tools, version comparison
|
└─ Local development script?
   └─ YES → `uvx --from . script.py`
      Examples: project-specific scripts

3. MCP Server Execution Patterns

Published Packages (No working directory needed):

{
  "servers": {
    "sqlite": {
      "command": "uvx",
      "args": ["mcp-server-sqlite", "--db-path", "/path/to/db"]
    }
  }
}

Local Development (Use --from flag):

{
  "servers": {
    "my-server": {
      "command": "uvx",
      "args": [
        "--from", "/absolute/path/to/project",
        "server.py",
        "--config", "config.json"
      ]
    }
  }
}

Key insight: --from flag IS the working directory reference for UVX.

4. Virtual Environment Management

UV works seamlessly with Python's built-in venv:

# Create virtual environment
python -m venv .venv

# Activate (Windows Git Bash)
. .venv/Scripts/activate

# Activate (Windows CMD)
.venv\Scripts\activate.bat

# Activate (Linux/Mac)
source .venv/bin/activate

# Install packages with UV
uv pip install -r requirements.txt

Common Workflows

Development Tools Setup

# Install development tools once
uv tool install black
uv tool install flake8
uv tool install mypy
uv tool install pytest

# Use daily
black .
flake8 src/
mypy src/
pytest tests/

MCP Server Usage

# Test published MCP servers
uvx mcp-server-sqlite --db-path test.db
uvx mcp-server-git --repository /path/to/repo

# Local MCP server development
uvx --from /path/to/project server.py --env config.env

Project Initialization

# Create new project with UV
uv init my-project
cd my-project

# Add dependencies
uv add requests fastapi

# Run project
uv run python main.py

Python Version Management

# List available Python versions
uv python list

# Install default Python version (3.14 in UV 0.9.6+)
uv python install

# Install specific Python version
uv python install 3.12
uv python install 3.13

# Use in project
uv python pin 3.12

Note: As of UV 0.9.6, Python 3.14 is the default version. If you need Python 3.13 or earlier, explicitly specify the version.

Inline Script Dependencies (PEP 723)

UV supports defining dependencies directly in Python script comments:

#!/usr/bin/env -S uv run --script
# /// script
# dependencies = [
#   "requests",
#   "pandas",
# ]
# ///

import requests
import pandas as pd

# Your code here

Run with automatic dependency installation:

# UV installs dependencies automatically
uv run script.py

Benefits:

• Self-contained single-file scripts
• No pyproject.toml needed
• Easy sharing and distribution
• Perfect for utilities and automation

See Inline Script Metadata Reference for comprehensive examples including MCP servers, web applications, data processing, and CLI tools.

Integration Patterns

VS Code MCP Configuration

For .vscode/mcp.json or user settings:

{
  "servers": {
    "published-server": {
      "type": "stdio",
      "command": "uvx",
      "args": ["mcp-server-sqlite", "--db-path", "${workspaceFolder}/db.sqlite"]
    },
    "local-dev": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from", "${workspaceFolder}",
        "src/server.py"
      ]
    }
  }
}

Continue IDE Configuration

For .continue/config.json:

{
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "uvx",
          "args": ["mcp-server-fetch"]
        }
      }
    ]
  }
}

GitHub Actions CI/CD

- name: Setup UV
  uses: astral-sh/setup-uv@v1

- name: Install dependencies
  run: uv pip install -r requirements.txt

- name: Run tests
  run: uv run pytest

Best Practices

Tool Management

DO:

• Use uv tool install for development tools used frequently
• Use uvx for MCP servers (follows community patterns)
• Keep tools isolated in their own environments
• Regularly upgrade tools with uv tool upgrade --all

DON'T:

• Use global pip for CLI tools (causes dependency conflicts)
• Install MCP servers with uv tool install (against community patterns)
• Use uvx for daily development tools (unnecessary overhead)
• Mix pip and uv tool installations

MCP Server Patterns

DO:

• Use UVX for all MCP server execution
• Use --from for local development
• Pin versions for production ([email protected])
• Use environment variables for configuration

DON'T:

• Install MCP servers globally
• Mix working directory approaches
• Use @latest in production (unstable)
• Forget to specify absolute paths with --from

Virtual Environments

DO:

• Use python -m venv for project environments
• Activate before installing packages
• Use uv pip install for faster package installation
• Document activation commands in README

DON'T:

• Install packages globally
• Mix venv and system Python packages
• Forget to activate before development
• Commit .venv directory to version control

Performance Characteristics

UV's performance advantages:

• 10-100x faster than pip for package operations
• Parallel downloads and installations
• Global cache with deduplication
• Rust-powered dependency resolution
• Disk-efficient storage with hard links

Typical operation times:

• Package installation: 100-1000x faster than pip
• Dependency resolution: Near-instant for cached packages
• Virtual environment creation: <1 second
• UVX first run: Package download time + execution
• UVX cached run: <1 second startup

Troubleshooting

Common Issues

"spawn uvx ENOENT" Error:

• UV/UVX not in PATH
• Solution: Reinstall UV or add to PATH manually

Package Not Found:

• Check package name on PyPI
• For local development, verify --from path
• Ensure pyproject.toml exists

Permission Errors:

• UV cache directory not writable
• Solution: Check permissions on ~/.cache/uv/

Version Conflicts:

• Multiple Python versions
• Solution: Use uv python pin to set project version

See detailed troubleshooting in:

• Installation & Setup Reference
• Tool Management Reference
• MCP Integration Reference

Reference Documentation

This skill includes detailed reference documentation:

1. Recent Changes ⭐ NEW

   • Latest version information (0.9.7+)
   • Python 3.14 default and free-threading support
   • New features and breaking changes
   • Version compatibility matrix
   • Upgrade guidance

2. Installation & Setup

   • Installation methods (Windows, Linux, Mac)
   • Virtual environment setup
   • Platform-specific considerations

3. Tool Management

   • UV tool install vs UVX comparison
   • Persistent vs temporary execution
   • Maintenance workflows

4. MCP Integration

   • Published package patterns
   • Local development with --from
   • VS Code and IDE configuration

5. Python Environment

   • Python version management
   • System paths (pyenv, uv, system)
   • Cross-platform compatibility

6. Inline Script Metadata

   • PEP 723 inline dependencies in comments
   • Single-file scripts with automatic dependency management
   • MCP servers, web apps, and CLI tools
   • Best practices and troubleshooting

7. Examples

   • Real-world GitHub configurations
   • Common workflow patterns
   • Anti-patterns to avoid

External Resources

• UV Official Documentation: https://docs.astral.sh/uv/
• UV GitHub Repository: https://github.com/astral-sh/uv
• MCP Official Documentation: https://modelcontextprotocol.io/
• MCP Servers Repository: https://github.com/modelcontextprotocol/servers
• VS Code MCP Support: https://code.visualstudio.com/docs/copilot/chat/mcp-servers

Migration Guides

From pip

# Old way
pip install requests

# New way
uv pip install requests

From pipx

# Old way
pipx install black

# New way
uv tool install black

From poetry

# Old way
poetry add requests
poetry install

# New way
uv add requests
uv sync

Summary

UV provides a unified, fast, and modern approach to Python package management. The key to effective UV usage is:

1. Understand the tool landscape: uv pip, uv tool, uvx each serve specific purposes
2. Follow community patterns: Use UVX for MCP servers, uv tool for development tools
3. Leverage isolation: Each tool gets its own environment preventing conflicts
4. Use --from for local development: Essential pattern for MCP server development
5. Keep tools updated: Regular maintenance prevents issues

By following these patterns and utilizing the reference documentation, you'll have a clean, efficient, and maintainable Python development environment.