run_notebook · Databricks MCP Server MCP: Risk & Policy

WHAT IT DOES

What run_notebook does on Databricks MCP Server

AI agents invoke run_notebook 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.

WHY IT NEEDS A POLICY

Why run_notebook needs a policy

This tool executes code (the notebook) on a Databricks cluster with configurable parameters. The effects are not predetermined—they depend on what the notebook does and what parameters are passed. While the notebook itself may contain read, write, or destructive operations, this tool is the mechanism that triggers arbitrary code execution.

From the tool's definition Tool name is 'run_notebook' and description states 'Submit a one-time notebook run with parameters'. The ability to run a notebook with arbitrary parameters allows execution of code whose effects depend on the notebook contents and parameter values.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

run_notebook Rate-limited

run_notebook 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": {
    "run_notebook": {
      "limits": [
        {
          "counter": "run_notebook_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Databricks MCP Server, so you can run agents against it safely.

FAQ

Questions about run_notebook

What does the run_notebook tool do? +

Submit a one-time notebook run with parameters: notebook_path (required), existing_cluster_id (optional), base_parameters (optional). 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 run_notebook? +

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

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

Can I rate-limit run_notebook? +

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

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

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