AI agents call delete_job to permanently remove resources in Databricks MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Deleting a job is an irreversible operation that removes a defined workflow from the Databricks workspace. This cannot be undone and affects scheduled or on-demand execution of data processing tasks. While not as critical as deleting data itself (which would be more severe), job deletion prevents future work and could disrupt data pipelines if critical jobs are removed.
From the tool's definition Tool name: 'delete_job'; description: 'Delete a Databricks job'. The verb 'delete' combined with the irreversible nature of removing a job from Databricks indicates destructive action.
Documented attack patterns abuse exactly the kind of access delete_job gives an agent:
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 delete_job:
{
"version": "1",
"default": "deny",
"hide": [
"delete_job"
]
}delete_job disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
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Delete a Databricks job. 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.
Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for delete_job: 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.
delete_job is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the delete_job 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.
Set action: deny in the PolicyLayer policy for delete_job. 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.
delete_job is provided by the Databricks MCP Server MCP server (markov-kernel/databricks-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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.
Free to start. No card required.
38 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.