terminate_cluster

THE RISK

Critical Risk

Why terminate_cluster needs a policy

Terminating a cluster is an irreversible disruptive action that stops all running workloads on that cluster. While the cluster configuration may be retained for restart, any in-progress jobs, sessions, and ephemeral state are permanently lost. This maps to Destructive. Severity is high because misuse could abort critical pipelines and disrupt dependent workflows across an organization.

From the tool's definition terminate_cluster — 'Terminate a Databricks cluster'

Documented attack patterns abuse exactly the kind of access terminate_cluster gives an agent:

POLICY

How to control terminate_cluster

PolicyLayer is an MCP gateway — it sits between your AI agents and Databricks MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for terminate_cluster:

policy.json

{
  "version": "1",
  "default": "deny",
  "hide": [
    "terminate_cluster"
  ]
}

terminate_cluster disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.

Create a free account and register Databricks MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

RESTRICT THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

More Databricks MCP Server tools

Execute execute_sql Execute a SQL statement with parameters: statement (required), warehouse_id (required), ca Execute run_job Run a Databricks job with parameters: job_id (required), notebook_params (optional) Execute start_cluster Start a terminated Databricks cluster with parameter: cluster_id (required) Write create_cluster Create a new Databricks cluster with parameters: cluster_name (required), spark_version (r Read get_cluster Get information about a specific Databricks cluster with parameter: cluster_id (required) Read list_clusters List all Databricks clusters Read list_files List files and directories in a DBFS path with parameter: dbfs_path (required) Read list_jobs List all Databricks jobs

All 11 Databricks MCP Server tools →

Destructive tools on other servers

Android Forensics ADB MCP Server create_full_backup 0xarchive web3_revoke_key Frida Game Hacking MCP clear_scan cPanel MCP Server clear_spam_box

Go deeper

Critical-risk tools in Databricks MCP Server → Every critical-risk tool this server exposes, in one view.
All critical-risk MCP tools → Every critical-risk tool across the catalogue, and the policy pattern that contains them.
The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Blast radius → How much damage one compromised or confused agent can do — and how to bound it.
Tool call approval → Approval gates on individual tool calls: auto-allow, prompt, or deny.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about terminate_cluster

What does the terminate_cluster tool do? +

Terminate a Databricks cluster with parameter: cluster_id (required). It is categorised as a Destructive tool in the Databricks MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.

How do I enforce a policy on terminate_cluster? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for terminate_cluster: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Databricks MCP Server. Nothing to install.

What risk level is terminate_cluster? +

terminate_cluster is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.

Can I rate-limit terminate_cluster? +

Yes. Add a rate_limit block to the terminate_cluster rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block terminate_cluster completely? +

Set action: deny in the PolicyLayer policy for terminate_cluster. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides terminate_cluster? +

terminate_cluster is provided by the Databricks MCP Server MCP server (justtryai/databricks-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Databricks MCP Server tool call.

Start from Databricks MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →