Update an existing job in the Databricks workspace.
AI agents use update_job 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.
The tool modifies job configuration in a Databricks workspace, which is a reversible operation (the job can be updated again to previous state). This is a Write operation rather than Destructive (no deletion/purge) or Execute (no arbitrary code execution triggered).
From the tool's definition Tool name 'update_job' and description 'Update an existing job in the Databricks workspace' indicate modification of existing data/configuration without deletion or reversal constraints.
Documented attack patterns abuse exactly the kind of access update_job 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 update_job:
{
"version": "1",
"default": "deny",
"tools": {
"update_job": {
"limits": [
{
"counter": "update_job_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_job 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.
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Update an existing job in the Databricks workspace. 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.
Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for update_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.
update_job is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update_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.
Set action: deny in the PolicyLayer policy for update_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.
update_job 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.