repair_run

A execute tool on the Databricks MCP server.

SERVERDatabricks MCP Server SOURCEandresgarciasobrado91/databricks-mcp-server

High RISK CLASS

Category Execute

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/andresgarciasobrado91-databricks-mcp-server/repair-run.md

WHAT IT DOES

What repair_run does on Databricks MCP Server

AI agents invoke repair_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.

RISK

Why repair_run is rated High

Although the description is empty (lowering confidence from 0.85 to 0.75), the tool name and Databricks context strongly suggest this triggers job execution or restart logic. Job restarts/repairs can have significant side effects depending on the job's operations (data processing, writes, external calls), making it Execute rather than Write.

From the tool's definition Tool name 'repair_run' combined with context of Databricks job management suggests executing or restarting a job run. The sibling tools include 'execute_sql' and 'create_cluster', indicating this server manages active operations.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs repair_run safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Databricks MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For repair_run, this is the rule to start with:

repair_run Rate-limited

repair_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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "repair_run": {
      "limits": [
        {
          "counter": "repair_run_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Databricks MCP Server, apply this rule, and every repair_run call is checked against it from then on.

FAQ

Questions about repair_run

What does the repair_run tool do? +

repair_run is a execute tool on the Databricks MCP Server MCP server. 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.

How do I enforce a policy on repair_run? +

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

What risk level is repair_run? +

repair_run is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit repair_run? +

Yes. Add a rate_limit block to the repair_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.

How do I block repair_run completely? +

Set action: deny in the PolicyLayer policy for repair_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.

What MCP server provides repair_run? +

repair_run is provided by the Databricks MCP Server MCP server (andresgarciasobrado91/databricks-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

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