uv-mcp

name	uv-mcp
description	UV command automation and project lifecycle management patterns powered by the uv-mcp server
license	MIT
compatibility	opencode
metadata	{"related_python_guidelines":"For general Python development standards","related_python_cli":"For CLI scaffolding patterns","related_mcp_servers":"For MCP integration best practices"}

UV MCP

What I Do

Describe how to steer the uv-mcp server so natural language requests become precise uv workflows. This skill focuses on diagnosing environments, managing dependencies, controlling Python runtimes, and building artifacts with uv while keeping the workspace healthy.

Core Workflows

Environment Health & Setup

Intent	Tool	What Happens
Diagnose failures or missing environments	diagnose_environment	Confirms pyproject.toml, virtualenv presence, lock sync status, and reports remediation steps.
Repair broken setups	repair_environment	Creates .venv, installs Python, and syncs dependencies automatically.
Verify/install uv itself	check_uv_installation, install_uv	Checks uv availability and returns platform-specific install steps when missing.

> "Diagnose the environment"
# Use output to confirm pyproject + venv state
> "Repair the environment"
# Follow-up diagnostics ensure issues are resolved

Dependency Management

Scenario	Tool	Notes
Add/remove libraries	add_dependency, remove_dependency	Supports --dev, optional dependency groups, and updates both config + env.
Keep env aligned with uv.lock	sync_environment	Syncs or upgrades locked versions; run after lockfile updates.
Inspect or refresh packages	check_outdated_packages, show_package_info	Surfaces available upgrades and in-depth metadata for any package.

Project Inspection

• list_dependencies: Lists installed packages; request tree mode for transitive view.
• analyze_dependency_tree: Visualizes dependency graph depth to spot heavy branches before refactors.

Runtime Management

Action	Tool	Guidance
List installed interpreters	list_python_versions	Shows versions uv already manages.
Install new interpreter	install_python_version	Downloads and activates the requested Python release.
Pin project runtime	pin_python_version	Updates .python-version to keep CI/CD aligned.

Project Lifecycle

• init_project: Scaffold a fresh uv-enabled project, ideal for greenfield work.
• export_requirements: Emit requirements.txt for platforms that expect pip-compatible manifests.

Build & Distribution (uv ≥ 0.6.4)

Need	Tool	Tips
Create wheels/sdists	build_project	Choose wheel-only or sdist-only, customize output dir, capture artifact paths.
Refresh lockfile without installing	lock_project	Useful after manual pyproject.toml edits or before committing dependency changes.
Clear uv cache	clear_cache	Fixes checksum mismatches or frees disk space; target entire cache or a single package.

Diagnostic Workflow Template

1. diagnose_environment
2. Review reported issues.
3. repair_environment
4. diagnose_environment again.
5. If still broken: clear_cache → lock_project → sync_environment.

Error Handling & Troubleshooting

• uv-mcp returns structured errors (error, error_code, suggestion). Echo them in summaries so humans know the auto-remediation path.
• Common scenarios:
  • UV not installed → run install_uv.
  • Missing packages → sync_environment.
  • Version conflicts → clear_cache then lock_project and sync_environment.
  • Corrupted artifacts → clear_cache for that package and re-sync.

When to Use Me

• Onboarding or repairing uv-based projects without manual shell work.
• Automating dependency chores (install, remove, upgrade) through MCP.
• Managing Python runtimes inside CI/CD or multi-OS fleets.
• Preparing releases: lockfiles, builds, requirements.txt exports.

Best Practices

1. Diagnose before repair: Always capture the initial state so changes are auditable.
2. Sync after mutations: Any add_dependency / lock_project call should be followed by sync_environment to keep .venv aligned.
3. Pin intentionally: Use pin_python_version once a runtime is validated by CI.
4. Cache hygiene: Run clear_cache when checksum or corruption errors show up, then re-sync immediately.
5. Capture artifacts: After build_project, record the returned artifact list (wheel + sdist paths) in release notes.

Integration Patterns

• With python-guidelines: Apply linting/testing standards after uv-mcp modifies dependencies.
• With python-cli: Use init_project + dependency adds to scaffold CLIs rapidly.
• With mcp-servers: Document uv-mcp availability in .vscode/mcp.json and keep tool lists lean.

Use this skill whenever the uv-mcp server is the fastest path to maintaining healthy uv environments without leaving the IDE context.