databricks_stop_warehouse

A execute tool on the Databricks MCP server.

SERVERDatabricks MCP Server SOURCEpypi:databricks-sdk-mcp

High RISK CLASS

Category Execute

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-stop-warehouse.md

WHAT IT DOES

What databricks_stop_warehouse does on Databricks MCP Server

AI agents invoke databricks_stop_warehouse 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.

RISK

Why databricks_stop_warehouse is rated High

Stopping a warehouse is an Execute-level action because it triggers an external operation (stopping Databricks compute infrastructure) with effects that depend on the current warehouse state. While not immediately destructive (the warehouse can be restarted), it immediately disrupts all active queries and workloads.

From the tool's definition Tool name 'databricks_stop_warehouse' indicates stopping a running warehouse resource. No description provided, but 'stop' is an operational action that terminates a compute resource, affecting active queries and operations.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs databricks_stop_warehouse safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Databricks MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For databricks_stop_warehouse, this is the rule to start with:

databricks_stop_warehouse Rate-limited

databricks_stop_warehouse 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "databricks_stop_warehouse": {
      "limits": [
        {
          "counter": "databricks_stop_warehouse_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Databricks MCP Server, apply this rule, and every databricks_stop_warehouse call is checked against it from then on.

FAQ

Questions about databricks_stop_warehouse

What does the databricks_stop_warehouse tool do? +

databricks_stop_warehouse is a execute tool on the Databricks MCP Server MCP server. 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.

How do I enforce a policy on databricks_stop_warehouse? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for databricks_stop_warehouse: 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 databricks_stop_warehouse? +

databricks_stop_warehouse is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit databricks_stop_warehouse? +

Yes. Add a rate_limit block to the databricks_stop_warehouse 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 databricks_stop_warehouse completely? +

Set action: deny in the PolicyLayer policy for databricks_stop_warehouse. 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 databricks_stop_warehouse? +

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

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