databricks_log_metric

A write tool on the Databricks MCP server.

SERVERDatabricks MCP Server SOURCEpypi:databricks-sdk-mcp

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-pramodbhatofficial-databricks-sdk-mcp/databricks-log-metric.md

WHAT IT DOES

What databricks_log_metric does on Databricks MCP Server

AI agents use databricks_log_metric 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.

RISK

Why databricks_log_metric is rated Medium

The name implies recording or persisting a metric (e.g., to an MLflow experiment run), which is a write operation. Without a description, confidence is low, but logging metrics is typically reversible/non-destructive. Severity is medium given potential impact on experiment tracking data.

From the tool's definition Tool name: 'databricks_log_metric' — 'log' suggests writing/recording a metric value; description is empty and uninformative.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs databricks_log_metric 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 databricks_log_metric, this is the rule to start with:

databricks_log_metric Rate-limited

databricks_log_metric 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": {
    "databricks_log_metric": {
      "limits": [
        {
          "counter": "databricks_log_metric_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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 databricks_log_metric call is checked against it from then on.

FAQ

Questions about databricks_log_metric

What does the databricks_log_metric tool do? +

databricks_log_metric is a write tool on the Databricks MCP Server MCP server. 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 databricks_log_metric? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for databricks_log_metric: 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 databricks_log_metric? +

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

Can I rate-limit databricks_log_metric? +

Yes. Add a rate_limit block to the databricks_log_metric 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 databricks_log_metric completely? +

Set action: deny in the PolicyLayer policy for databricks_log_metric. 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 databricks_log_metric? +

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

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