Stop a running DLT pipeline update.
AI agents invoke stop_pipeline_update to trigger actions in Databricks MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
Stopping a running pipeline update is an Execute action because it triggers an external operation with real-time effects on a data processing system. While not destructive (the pipeline can be restarted), it interrupts active computational work and has side effects dependent on the pipeline's state.
From the tool's definition Tool name contains 'stop' action targeting a 'pipeline update' system. Description states it will 'Stop a running DLT pipeline update,' which is an active operation that interrupts an ongoing process.
Documented attack patterns abuse exactly the kind of access stop_pipeline_update 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 stop_pipeline_update:
{
"version": "1",
"default": "deny",
"tools": {
"stop_pipeline_update": {
"limits": [
{
"counter": "stop_pipeline_update_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}stop_pipeline_update stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Stop a running DLT pipeline update. It is categorised as a Execute tool in the Databricks MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for stop_pipeline_update: 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.
stop_pipeline_update is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the stop_pipeline_update 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 stop_pipeline_update. 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.
stop_pipeline_update is provided by the Databricks MCP Server MCP server (pulkitxchadha/awesome-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.
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86 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.