start_cluster
Start a terminated Databricks cluster by providing its cluster ID to resume data processing and analytics workflows.
Instructions
Start a terminated Databricks cluster
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cluster_id | Yes |
Implementation Reference
- MCP tool handler for start_cluster that wraps the core API call, handles errors, and returns JSON response.
@mcp.tool() async def start_cluster(cluster_id: str) -> str: """Start a terminated Databricks cluster""" logger.info(f"Starting cluster: {cluster_id}") try: result = await clusters.start_cluster(cluster_id) return json.dumps(result) except Exception as e: logger.error(f"Error starting cluster: {str(e)}") return json.dumps({"error": str(e)}) - src/api/clusters.py:79-93 (helper)Core implementation that performs the actual Databricks API request to start the cluster.
async def start_cluster(cluster_id: str) -> Dict[str, Any]: """ Start a terminated Databricks cluster. Args: cluster_id: ID of the cluster to start Returns: Empty response on success Raises: DatabricksAPIError: If the API request fails """ logger.info(f"Starting cluster: {cluster_id}") return make_api_request("POST", "/api/2.0/clusters/start", data={"cluster_id": cluster_id})