Velero MCP Server
Provides read-only access to Velero backup and schedule resources in Kubernetes clusters, enabling AI agents to inspect backups, schedules, and generate Velero YAML manifests safely without write permissions.
README
Velero MCP Server
The Velero MCP Server is an open-source Model Context Protocol (MCP) server that exposes read-only, safe, structured access to Velero backup and schedule resources running inside any Kubernetes cluster.
It allows AI agents (ChatGPT, Claude, Cursor, GitHub Copilot, etc.) to:
- π Inspect Velero backups
- π Inspect Velero schedules
- π Generate Velero Backup YAML manifests
- π§© Access Velero data as MCP resources
- π Safely interact with your cluster in read-only mode
This project helps platform engineers automate workflows using AI while ensuring zero-risk, low-privilege, and read-only access to critical cluster configuration.
β Why This Project Exists
Velero is commonly used for:
- Kubernetes namespace & cluster backups
- Disaster recovery
- Cluster migrations
- Persistent volume snapshot management
But until now, no MCP server existed to expose Velero CRDs to LLM-based tools in a safe, structured way.
This project provides:
- A consistent API for querying Velero
- Strong typed models
- Complete read-only safety
- Guaranteed LLM-friendly output
- Ready integration with GitOps
π Features
π§ MCP Tools
list_velero_backups(namespace?: str)
Returns a list of Velero Backup CRs.
get_velero_backup(name: str, namespace?: str)
Returns a detailed structured backup object.
list_velero_schedules(namespace?: str)
Lists Velero Schedule CRs including cron, paused state, and last backup.
generate_velero_backup_yaml(...)
Generates read-only YAML for a Velero Backup.
π¦ MCP Resource Endpoints
| Resource | Description |
|---|---|
velero://backups |
All backups in default namespace |
velero://schedules |
All schedules in default namespace |
These allow LLMs to explore Velero state without calling tools.
π Architecture
- Python 3.10+
- MCP (official Model Context Protocol SDK)
- Kubernetes Python Client
- Pydantic models
- Safe, read-only design
- No
kubectl, no exec, no side effects
Flow:
MCP Client β Velero MCP Server β Kubernetes API β Velero CRDs
π Security Model
Designed to be 100% safe
The server never:
- Creates backups
- Runs restores
- Deletes backup objects
- Writes anything to Kubernetes
Only reads CRDs via the Kubernetes API.
RBAC Required:
get, list on:
- backups.velero.io
- schedules.velero.io
π₯ Installation
git clone https://github.com/YOUR-ORG/velero-mcp-server.git
cd velero-mcp-server
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
pip install .
For development:
pip install ".[dev]"
βοΈ Configuration
Environment Variables
| Variable | Description | Default |
|---|---|---|
KUBECONFIG |
Path to kubeconfig | auto |
VELERO_NAMESPACE |
Velero namespace | velero |
K8s auth order:
- In-cluster ServiceAccount
$KUBECONFIG~/.kube/config
βΆοΈ Running the Server
Start the server in stdio mode (required by MCP):
python -m velero_mcp_server.server
π§© Example MCP Client Configuration
ChatGPT MCP configuration
{
"mcpServers": {
"velero-mcp": {
"command": "python",
"args": ["-m", "velero_mcp_server.server"],
"env": {
"KUBECONFIG": "/path/to/kubeconfig",
"VELERO_NAMESPACE": "velero"
}
}
}
}
Claude Desktop
"mcpServers": {
"velero-mcp": {
"command": "python",
"args": ["-m", "velero_mcp_server.server"],
"env": {
"KUBECONFIG": "/path/to/kubeconfig",
"VELERO_NAMESPACE": "velero"
}
}
}
π§ͺ Example Usage (AI Agent)
List failed backups
βCall
list_velero_backupsand filter phase = Failed.β
Inspect a backup
βUse
get_velero_backupforprod-fulland tell me included namespaces.β
Generate a manifest
βGenerate a Velero backup YAML including namespaces
db,logging, TTL=168h.β
Produces:
apiVersion: velero.io/v1
kind: Backup
metadata:
name: prod-backup
namespace: velero
spec:
includedNamespaces:
- db
- logging
ttl: 168h
π Development
Run linters:
ruff check velero_mcp_server
Type-check:
mypy velero_mcp_server
Run tests:
pytest
π€ Contributing
We welcome contributions of all kinds:
- Add Velero Restore / VolumeSnapshot support
- Improve error handling
- Add more MCP resources
- Add a Helm chart
- Add logging or metrics
- Improve documentation
Read the full CONTRIBUTING.md.
π License
This project is licensed under the MIT License, allowing:
- Commercial use
- Private use
- Modification
- Distribution
πΊ Roadmap
Planned improvements:
- Add support for Restore CRDs
- Snapshot objects
- Restore impact analysis
- Write-enabled mode (behind strict flags)
- Publish to PyPI
- Helm chart for cluster deployment
If you'd like to help shape the direction, please open an issue!
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Neon Database
MCP server for interacting with Neon Management API and databases