AI agents use create_repo 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.
This tool creates or clones repositories, which modifies the workspace state reversibly. It does not execute arbitrary code, delete data irreversibly, or move money. The action is reversible (repos can be deleted), and the blast radius is contained to repository creation/management rather than data destruction or code execution.
From the tool's definition Tool name is 'create_repo' and description states 'Create or clone a repo', indicating it creates new repository resources in the Databricks workspace.
Documented attack patterns abuse exactly the kind of access create_repo 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 create_repo:
{
"version": "1",
"default": "deny",
"tools": {
"create_repo": {
"limits": [
{
"counter": "create_repo_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}create_repo 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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Create or clone a repo. 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 create_repo: 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.
create_repo 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 create_repo 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 create_repo. 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.
create_repo 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.