Introduction

Introduction

UV-MCP is a Model Context Protocol (MCP) server designed to bridge the gap between Large Language Models (LLMs) and the uv Python package manager. It enables AI assistants to autonomously manage Python environments, dependencies, and project lifecycles with the speed and reliability of Rust-based tooling.

Value Proposition

Modern Python development involves complex environment management—virtual environments, lockfiles, and dependency resolution. UV-MCP abstracts this complexity, allowing developers to interact with their projects using natural language while ensuring rigorous adherence to best practices.

• Speed: Leverages uv for near-instant package resolution and installation.
• Correctness: Enforces reproducible environments via universal lockfiles (uv.lock).
• Autonomy: Empowers agents to diagnose issues and perform self-healing operations without user intervention.

Core Concepts

Model Context Protocol (MCP)

The Model Context Protocol is the standard interface that allows AI models to execute code and retrieve context from local systems.

• Server: UV-MCP runs locally, exposing discrete Tools (e.g., add_dependency, repair_environment) to the client.
• Client: The AI interface (e.g., Gemini CLI, Claude Desktop) consumes these tools to fulfill user requests.

The uv Engine

UV-MCP is built on top of uv, an extremely fast Python package installer and resolver. It does not reinvent package management; rather, it exposes uv’s capabilities to the AI context layer.

Capabilities

UV-MCP provides a comprehensive suite of tools for the entire project lifecycle:

1. Environment Health:

   • Diagnostics: Comprehensive checks for project structure, interpreter validity, and dependency synchronization.
   • Repair: Automated remediation of common issues (e.g., missing venvs, desynced lockfiles).

2. Dependency Management:

   • Add, remove, and update packages.
   • Manage development and optional dependency groups.
   • Export locked dependencies to standard formats.

3. Introspection:

   • Analyze dependency trees and hierarchies.
   • Audit outdated packages and security implications.
   • Retrieve detailed metadata for installed libraries.

4. Project Scaffolding:

   • Initialize new applications or libraries with industry-standard templates.
   • Manage Python version pinning and installation.

Next Steps

To begin using UV-MCP, proceed to the following guides:

• Installation: Setup instructions for your operating system and AI client.
• Usage: Common workflows and prompt engineering examples.
• Tool Reference: Technical API documentation for all available tools.