AI agents invoke submit_job_run 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.
Submitting a job run causes external computation to execute on the Databricks platform. The effects are determined by the job's code/notebooks/scripts and could include data transformations, writes, or other side effects. This is fundamentally an Execute action. Severity is high because a misused job submission could consume significant compute resources, modify data, or trigger unintended workflows at scale.
From the tool's definition 'Submit a new job run' — triggers execution of a Databricks job, initiating compute operations whose effects depend on the job's definition and arguments.
Documented attack patterns abuse exactly the kind of access submit_job_run gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Databricks MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for submit_job_run:
{
"version": "1",
"default": "deny",
"tools": {
"submit_job_run": {
"limits": [
{
"counter": "submit_job_run_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}submit_job_run 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.
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Submit a new job run. 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.
Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for submit_job_run: 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.
submit_job_run is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the submit_job_run 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.
Set action: deny in the PolicyLayer policy for submit_job_run. 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.
submit_job_run is provided by the Databricks MCP Server MCP server (pulkitxchadha/awesome-databricks-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Databricks MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
86 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.