databricks_update_share

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-update-share.md

WHAT IT DOES

What databricks_update_share does on Databricks MCP Server

AI agents use databricks_update_share 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_update_share is rated Medium

The 'update' verb indicates reversible modification rather than deletion or execution. Databricks shares control data access permissions and collaboration—updating them could affect data visibility and access controls for multiple users, warranting 'high' severity. Confidence is moderate (0.75) due to empty description; if the tool modifies sensitive access controls, severity could escalate to 'critical'.

From the tool's definition Tool name 'databricks_update_share' indicates modification of share objects. Description is empty, limiting specificity.

Attacks that exploit this kind of access

RECOMMENDED RULE

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

databricks_update_share Rate-limited

databricks_update_share 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_update_share": {
      "limits": [
        {
          "counter": "databricks_update_share_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_update_share call is checked against it from then on.

FAQ

Questions about databricks_update_share

What does the databricks_update_share tool do? +

databricks_update_share 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_update_share? +

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

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

Can I rate-limit databricks_update_share? +

Yes. Add a rate_limit block to the databricks_update_share 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_update_share completely? +

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

databricks_update_share 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.

KEEP EXPLORING

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