update_repo · Databricks MCP Server MCP: Risk & Policy

WHAT IT DOES

What update_repo does on Databricks MCP Server

AI agents use update_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.

WHY IT NEEDS A POLICY

Why update_repo needs a policy

This tool creates or modifies repository branch/tag references reversibly. While it changes version control state, it is not destructive (branches/tags can be recreated or reassigned), nor does it execute arbitrary code or move financial resources. It falls into the Write category as a reversible modification operation.

From the tool's definition Tool name 'update_repo' and description 'Update repo branch' indicate modification of repository state. Parameters 'repo_id, branch or tag' allow changing branch references or tags, which are reversible write operations on version control metadata.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

update_repo Rate-limited

update_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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "update_repo": {
      "limits": [
        {
          "counter": "update_repo_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Databricks MCP Server, so you can run agents against it safely.

FAQ

Questions about update_repo

What does the update_repo tool do? +

Update repo branch with parameters: repo_id, branch or tag. 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.

How do I enforce a policy on update_repo? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for update_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.

What risk level is update_repo? +

update_repo is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit update_repo? +

Yes. Add a rate_limit block to the update_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.

How do I block update_repo completely? +

Set action: deny in the PolicyLayer policy for update_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.

What MCP server provides update_repo? +

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