mcp_create_and_run_serverless_dlt_pipeline

WHAT IT DOES

What mcp_create_and_run_serverless_dlt_pipeline does on Databricks MCP Server

AI agents invoke mcp_create_and_run_serverless_dlt_pipeline 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 mcp_create_and_run_serverless_dlt_pipeline needs a policy

This tool does more than just write/configure a resource; it actively runs a pipeline (executes compute workloads on Databricks). The 'run' aspect puts it in Execute category. Severity is high because an AI agent misusing this tool could spin up costly serverless compute jobs, execute arbitrary notebook code, and consume significant cloud resources with potentially broad data impact.

From the tool's definition 'Create and run a serverless Delta Live Tables pipeline' — the tool both creates a pipeline and immediately triggers execution of it

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

mcp_create_and_run_serverless_dlt_pipeline Rate-limited

mcp_create_and_run_serverless_dlt_pipeline 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": {
    "mcp_create_and_run_serverless_dlt_pipeline": {
      "limits": [
        {
          "counter": "mcp_create_and_run_serverless_dlt_pipeline_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 mcp_create_and_run_serverless_dlt_pipeline

What does the mcp_create_and_run_serverless_dlt_pipeline tool do? +

Create and run a serverless Delta Live Tables pipeline for a given notebook path. Returns pipeline/update IDs and Databricks URLs for monitoring. 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 mcp_create_and_run_serverless_dlt_pipeline? +

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

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

Can I rate-limit mcp_create_and_run_serverless_dlt_pipeline? +

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

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

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