AI agents call list_job_runs to retrieve information from Databricks MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves historical information about job execution without creating, modifying, or deleting any data. It is a read-only query operation that returns metadata about completed or in-progress runs. The blast radius of misuse is minimal—an agent could only over-query or enumerate job runs, which does not compromise data integrity or trigger unwanted operations.
From the tool's definition Tool name 'list_job_runs' and description 'List recent job runs' indicate a query/retrieval operation with no modifications or side effects.
Documented attack patterns abuse exactly the kind of access list_job_runs 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 list_job_runs:
{
"version": "1",
"default": "deny",
"tools": {
"list_job_runs": {}
}
}list_job_runs is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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List recent job runs. It is categorised as a Read tool in the Databricks MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for list_job_runs: 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.
list_job_runs is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the list_job_runs 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 list_job_runs. 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.
list_job_runs is provided by the Databricks MCP Server MCP server (markov-kernel/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.
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38 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.