KubeStellar A2A / MCP

![CI](https://github.com/kubestellar/a2a/actions/workflows/ci.yml) ![Python](https://www.python.org/downloads/) ![License](./LICENSE)

📚 View Full Documentation

╭─────────────────────────────────────────────────────────────────────────────────────────────╮
│  ██╗  ██╗██╗   ██╗██████╗ ███████╗███████╗████████╗███████╗██╗     ██╗      █████╗ ██████╗  │
│  ██║ ██╔╝██║   ██║██╔══██╗██╔════╝██╔════╝╚══██╔══╝██╔════╝██║     ██║     ██╔══██╗██╔══██╗ │
│  █████╔╝ ██║   ██║██████╔╝█████╗  ███████╗   ██║   █████╗  ██║     ██║     ███████║██████╔╝ │
│  ██╔═██╗ ██║   ██║██╔══██╗██╔══╝  ╚════██║   ██║   ██╔══╝  ██║     ██║     ██╔══██║██╔══██╗ │
│  ██║  ██╗╚██████╔╝██████╔╝███████╗███████║   ██║   ███████╗███████╗███████╗██║  ██║██║  ██║ │
│  ╚═╝  ╚═╝ ╚═════╝ ╚═════╝ ╚══════╝╚══════╝   ╚═╝   ╚══════╝╚══════╝╚══════╝╚═╝  ╚═╝╚═╝  ╚═╝ │
│                       Multi-Cluster Kubernetes Management Agent                             │
╰─────────────────────────────────────────────────────────────────────────────────────────────╯

https://github.com/user-attachments/assets/fd5746f9-6620-4aeb-8150-dd9bf9eab694

CLI Setup (uv)

# Enable virtual environment
uv venv

# Install with uv
uv pip install -e ".[dev]"

# Run commands
uv run kubestellar --help
uv run kubestellar list-functions
uv run kubestellar execute <function_name>
uv run kubestellar agent  # Start interactive AI agent

kubectl Plugin Installation

You can install and use this project as a kubectl plugin. Primary plugin name is kubestellar (for Krew and release binaries). We also ship a Python-installed alias a2a for convenience. kubectl discovers plugins via executables named kubectl-<name> on your PATH.

Installation options:

# Using uv tool (recommended for per-user install)
uv tool install .

# Using pipx (isolated virtualenv)
pipx install .

# Or directly from GitHub
pipx install 'git+https://github.com/kubestellar/a2a'

# Using pip (installs into current Python environment)
python -m pip install .

Usage:

# kubectl will find the plugin as long as the executable is on PATH
# Python-installed alias (uv/pipx/pip):
kubectl a2a --help
kubectl a2a list-functions
kubectl a2a execute <function_name> -P key=value
kubectl a2a agent

# Krew or release binary name:
kubectl kubestellar --help
kubectl kubestellar list-functions
kubectl kubestellar execute <function_name> -P key=value
kubectl kubestellar agent

Notes:

The plugin entrypoint is provided by the executable kubectl-a2a, which is installed via the Python package entry points. This makes kubectl a2a behave the same as running the kubestellar CLI directly.
With uv tool install, executables are placed under ~/.local/bin by default. Ensure it is on your PATH.

Install via Krew

Once a release is published, you can use the generated Krew manifest to install:

# Install from the generated manifest attached to a release (name: kubestellar.yaml)
kubectl krew install --manifest=kubestellar.yaml

# Use the plugin
kubectl kubestellar --help

To make installation available via the central krew-index and install like kubectl krew install kubestellar, submit a PR to https://github.com/kubernetes-sigs/krew-index with the kubestellar.yaml manifest from your release.

Direct install (no package manager)

You can install the plugin by placing a binary named kubectl-kubestellar (or kubectl-kubestellar.exe on Windows) on your PATH. Alternatively, the Python package installs kubectl-a2a which kubectl also discovers.

Option A — use a release binary (kubectl-kubestellar):

# Download the tarball for your OS/arch from the latest Release
tar -xzf kubectl-kubestellar-<os>-<arch>.tar.gz
chmod +x kubectl-kubestellar
mv kubectl-kubestellar ~/.local/bin/   # or any dir on your PATH

# verify
which kubectl-kubestellar
kubectl plugin list | grep kubestellar || true
kubectl kubestellar --help

Option B — build locally and copy to PATH (kubectl-kubestellar):

uv sync --dev
uv pip install pyinstaller
uv run pyinstaller --onefile --name kubectl-kubestellar --distpath dist --workpath build packaging/entry_kubectl_a2a.py
install -m 0755 dist/kubectl-kubestellar ~/.local/bin/kubectl-kubestellar

Option C — reuse the Python entrypoint by symlink (alias a2a):

# If you've installed the package via uv tool/pipx and have `kubestellar` on PATH,
# create a symlink named kubectl-kubestellar pointing to it
ln -sf "$(command -v kubestellar)" ~/.local/bin/kubectl-kubestellar
kubectl kubestellar --help

Windows:

Use the .exe from the Windows release archive and place it in a directory on your %PATH%.
Or create kubectl-a2a.bat forwarding to kubestellar.exe if using a symlink alternative isn’t convenient.

How kubectl plugin discovery works (Kubectl Plugins)

kubectl discovers plugins by searching your PATH for executables named kubectl-<name> (per the official docs). Examples: kubectl-kubestellar, kubectl-a2a.
When you run kubectl <name> ..., kubectl executes the first kubectl-<name> found on PATH and passes through the arguments.
List discovered plugins with kubectl plugin list.
Krew is a plugin manager that installs such binaries under its own path; kubectl krew install <name> makes <name> available as kubectl <name>.
This project provides kubectl-kubestellar (for Krew and binary releases) and kubectl-a2a (Python entrypoint). Once on your PATH, use kubectl kubestellar ... or kubectl a2a ....

Reference: https://kubernetes.io/docs/tasks/extend-kubectl/kubectl-plugins/

Get the latest version

For users who installed via uv tool from GitHub:

# Install latest from main
uv tool install --upgrade 'git+https://github.com/kubestellar/a2a@main'

# Or upgrade an existing install (uses original source spec)
uv tool upgrade kubestellar

For pipx installs:

pipx upgrade kubestellar
# or reinstall from GitHub
pipx install --force 'git+https://github.com/kubestellar/a2a@main'

Releasing

This repo includes an automated release workflow that builds platform-specific plugin binaries and publishes a Krew manifest.

Steps:

Create a version tag and push, e.g.: git tag v0.1.0 && git push origin v0.1.0
Or run manually via Actions → Release → Run workflow (optional tag input).
Or publish a GitHub Release with tag vX.Y.Z to trigger the workflow.
GitHub Actions workflow .github/workflows/release.yml runs and produces:
Tarballs for kubectl-kubestellar on Linux amd64, macOS amd64/arm64, Windows amd64
SHA256 checksums
kubestellar.yaml Krew manifest with versioned asset URLs and checksums
A GitHub Release containing the above assets

Users can then install via Krew using the attached manifest, or you can submit it to the central krew-index.

Troubleshooting release CI

If the workflow didn’t run: ensure tag matches v..*, Actions are enabled, and branch protections allow workflows.
For manual runs: use the workflow_dispatch entry. Optionally provide the tag input.
For Release events: make sure the release is “published” (not draft/prerelease) and has a tag like vX.Y.Z.

Local Plugin Testing

You can test kubectl a2a locally in two ways.

Using uv tool (recommended):

# From the repo root
uv tool uninstall kubectl-a2a || true
uv tool uninstall kubestellar || true
uv tool install .

# Ensure ~/.local/bin is on PATH, then verify
which kubectl-a2a
kubectl plugin list | grep a2a || true
kubectl a2a --help
kubectl a2a list-functions

# Debug kubectl plugin discovery if needed
kubectl -v=6 a2a --help

Using a locally built single-file binary (optional):

# Build the binary
uv sync --dev
uv pip install pyinstaller
uv run pyinstaller --onefile --name kubectl-a2a --distpath dist --workpath build packaging/entry_kubectl_a2a.py

# Put it on PATH for this shell and test
export PATH="$PWD/dist:$PATH"
kubectl plugin list | grep a2a || true
kubectl a2a --help

Notes:

If your shell caches command paths, run hash -r (bash/zsh) after replacing the binary.
On Windows, ensure dist/kubectl-a2a.exe is on your PATH.
If you use pip rather than pipx, ensure the Python scripts directory is on your PATH (e.g., ~/.local/bin on Linux, ~/Library/Python/<version>/bin on macOS, or the virtual environment's bin).
For isolated installs, pipx is the simplest way to get kubectl-a2a onto your PATH.

AI Provider Configuration

The agent supports multiple AI providers:

Available Providers

OpenAI (GPT-4, GPT-4o, etc.)
Google Gemini (gemini-1.5-flash, gemini-1.5-pro, etc.)

Setting Up API Keys

# Set OpenAI API key
uv run kubestellar config set-key openai YOUR_OPENAI_API_KEY

# Set Gemini API key  
uv run kubestellar config set-key gemini YOUR_GEMINI_API_KEY

# Set default provider
uv run kubestellar config set-default gemini

# List configured providers
uv run kubestellar config list-keys

# Show current configuration
uv run kubestellar config show

Using Different Providers

# Use default provider
uv run kubestellar agent

# Use specific provider
uv run kubestellar agent --provider gemini
uv run kubestellar agent --provider openai

MCP Server Setup

Add to MCP server (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "kubestellar": {
      "command": "uv",
      "args": ["run", "kubestellar-mcp"],
      "cwd": "/path/to/a2a"
    }
  }
}

Documentation

📖 Complete Documentation: https://kubestellar.io/docs/a2a

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Acknowledgments

Built with MCP SDK
Inspired by the KubeStellar project for multi-cluster Kubernetes management
Thanks to all contributors and the open-source community

---

Made with ❤️ by the KubeStellar community

KubeStellar A2A MCP Server