run_job

Run a Databricks job with parameters: job_id (string, required), job_parameters (dictionary, optional, job-level parameters)

SERVERDatabricks MCP Server SOURCEandresgarciasobrado91/databricks-mcp-server

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/andresgarciasobrado91-databricks-mcp-server/run-job.md

WHAT IT DOES

What run_job does on Databricks MCP Server

AI agents invoke run_job 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 run_job is rated High

This tool executes jobs on Databricks infrastructure. While the actual job logic is pre-defined (not arbitrary code execution like execute_sql), the outcome depends entirely on which job is selected and what parameters are passed. A malicious agent could trigger unintended jobs, modify data via job operations, or exhaust computational resources.

From the tool's definition Tool name 'run_job' combined with description 'Run a Databricks job' indicates execution of an external job whose effects depend on job_id and job_parameters arguments. This triggers pre-defined but potentially arbitrary logic on a remote cluster.

Attacks that exploit this kind of access

RECOMMENDED RULE

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

run_job Rate-limited

run_job 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_job": {
      "limits": [
        {
          "counter": "run_job_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 run_job call is checked against it from then on.

FAQ

Questions about run_job

What does the run_job tool do? +

Run a Databricks job with parameters: job_id (string, required), job_parameters (dictionary, optional, job-level parameters). 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_job? +

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

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

Can I rate-limit run_job? +

Yes. Add a rate_limit block to the run_job 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_job completely? +

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

run_job is provided by the Databricks MCP Server MCP server (andresgarciasobrado91/databricks-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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