mcp-server-uyuni

Model Context Protocol Server for Uyuni Server API.

Tools

• get_list_of_active_systems
• get_cpu_of_a_system
• get_all_systems_cpu_info
• check_system_updates
• check_all_systems_for_updates
• schedule_apply_pending_updates_to_system
• schedule_apply_specific_update
• get_systems_needing_security_update_for_cve
• get_systems_needing_reboot
• schedule_system_reboot
• cancel_action
• list_all_scheduled_actions

Usage

You need uv installed. See https://docs.astral.sh/uv

Once you have uv, install the dependencies with:

uv sync

You need to create the .venv/credentials with a content like this:

UYUNI_SERVER=192.168.1.124:8443
UYUNI_USER=admin
UYUNI_PASS=admin

Replace the values by the ones that make sense for you.

Then, you can use this command with an mcp-client:

uv run --env-file=.venv/credentials --directory PATH OF THIS CHECKOUT mcp-server-uyuni

Debug with mcp inspect

You can run

npx @modelcontextprotocol/inspector uv run --env-file=.venv/credentials --directory PATH OF THIS CHECKOUT mcp-server-uyuni

Use with Open WebUI

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG, making it a powerful AI deployment solution. More at https://docs.openwebui.com/

Setup Open WebUI

You need uv installed. See https://docs.astral.sh/uv

Start v0.6.10 (for MCP support we need a version >= 0.6.7)

 uv tool run [email protected] serve

Configure the OpenAI API URL by following these instructions:

https://docs.openwebui.com/getting-started/quick-start/starting-with-openai

For gemini, use the URL https://generativelanguage.googleapis.com/v1beta/openai and get the token API from the Google AI Studio https://aistudio.google.com/

Setup Open WebUI MCP Support

You need to create the .venv/credentials with a content like this:

UYUNI_SERVER=192.168.1.124:8443
UYUNI_USER=admin
UYUNI_PASS=admin

Then, you need a config.json with a content like this. Replace the values by the ones that make sense for you.

{
  "mcpServers": {
    "mcp-server-uyuni": {
      "command": "uv",
      "args": ["run", "--env-file=PATH OF THIS CHECKOUT/.venv/credentials","--directory","PATH OF THIS CHECKOUT","mcp-server-uyuni]
    }
  }
}

Then, you can start the Model Context Protocol to Open API proxy server:

uvx mcpo --port 9000  --config ./config.json

Add the tool

And then you can add the tool to the Open Web UI. See https://docs.openwebui.com/openapi-servers/open-webui#step-2-connect-tool-server-in-open-webui .

Note the url should be http://localhost/mcp-server-uyuni as explained in https://docs.openwebui.com/openapi-servers/open-webui#-optional-using-a-config-file-with-mcpo

Build

Docker build:

docker build -t  mcp-server-uyuni .

Then, instead of using uv run you can use docker run -i --rm --env-file .venv/credentials mcp-server-uyuni at any of the mcp-client configurations explained above.

Release Process

To create a new release for mcp-server-uyuni, follow these steps:

1. Update Documentation (README.md):
   • Ensure the list of available tools under the "## Tools" section is current and reflects all implemented tools in srv/mcp-server-uyuni/server.py.
   • Review and update any screenshots in the docs/ directory and their references in this README.md to reflect the latest UI or functionality, if necessary.
   • Verify all usage instructions and examples are still accurate.
2. Update Manual Test Cases (TEST_CASES.md):
   • Refer to the "How to Update for a New Tag/Release" section within TEST_CASES.md.
   • Add a new status column for the upcoming release version (e.g., Status (vX.Y.Z)).
   • Execute all relevant manual test cases against the code to be released.
   • Record the Pass, Fail, Blocked, or N/A status for each test case in the new version column.
3. Commit Changes: Commit all the updates to README.md, TEST_CASES.md, and any other changed files.
4. Update version in pyproject.toml: Use semantic versioning to set the new version.
5. Update CHANGELOG.md:
   • Generate the changelog using conventional-changelog-cli. If you don't have it installed globally, you can use npx.
   • The command to generate the changelog using the conventionalcommits preset and output it to CHANGELOG.md (prepending the new changes) is:

     npx conventional-changelog-cli -p conventionalcommits -i CHANGELOG.md -s

   • Review the generated CHANGELOG.md for accuracy and formatting.
   • Commit the updated CHANGELOG.md.
6. Create Git Tag: Create a new Git tag for the release (e.g., git tag vX.Y.Z). Follow semantic versioning rules.
7. Push Changes and Tags: Push your commits (including the changelog update) and the new tag to the repository (e.g., git push && git push --tags).
8. Automated Build and Push: Pushing the tag to GitHub will automatically trigger the "Docker Publish" GitHub Action. This action builds the Docker image and pushes it to the GitHub Container Registry (ghcr.io) with tags for the specific version (e.g., v0.3.0) and major.minor (e.g., v0.3). Pushing to main will update the latest tag.
9. Test the container: Pull the newly published image from ghcr.io and run the tests in TEST_CASES.md against it. docker run -i --rm --env-file .venv/credentials ghcr.io/uyuni-project/mcp-server-uyuni:VERSION (replace VERSION with the new tag).

License

MIT