databricks_update_pipeline

A write tool on the Databricks MCP server.

SERVERDatabricks MCP Server SOURCEpypi:databricks-sdk-mcp

Medium RISK CLASS

Category Write

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-update-pipeline.md

WHAT IT DOES

What databricks_update_pipeline does on Databricks MCP Server

AI agents use databricks_update_pipeline to create or update resources in Databricks MCP Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Databricks MCP Server environment.

RISK

Why databricks_update_pipeline is rated Medium

The 'update' verb indicates modification of pipeline configuration or state. This is a Write operation because updates are typically reversible (can be reverted to previous versions). Severity is high because misconfigured pipeline updates could disrupt data processing workflows and affect downstream systems.

From the tool's definition Tool name 'databricks_update_pipeline' contains the verb 'update', which modifies existing data reversibly. The tool operates within Databricks' pipeline management system as indicated by the server context listing pipeline-related tools.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs databricks_update_pipeline 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_update_pipeline, this is the rule to start with:

databricks_update_pipeline Rate-limited

databricks_update_pipeline stays usable, but capped: an agent stuck in a loop can't make hundreds of changes 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_update_pipeline": {
      "limits": [
        {
          "counter": "databricks_update_pipeline_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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_update_pipeline call is checked against it from then on.

FAQ

Questions about databricks_update_pipeline

What does the databricks_update_pipeline tool do? +

databricks_update_pipeline is a write tool on the Databricks MCP Server MCP server. It is categorised as a Write tool in the Databricks MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on databricks_update_pipeline? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for databricks_update_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 databricks_update_pipeline? +

databricks_update_pipeline is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit databricks_update_pipeline? +

Yes. Add a rate_limit block to the databricks_update_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 databricks_update_pipeline completely? +

Set action: deny in the PolicyLayer policy for databricks_update_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 databricks_update_pipeline? +

databricks_update_pipeline 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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