get_pipeline_run

THE RISK

Low Risk

Why get_pipeline_run needs a policy

This tool retrieves information about an existing pipeline run—it queries the state and details of a completed or in-progress operation. There are no side effects, no data modification, no code execution triggered, and no irreversible actions. This is a straightforward read operation that returns metadata about a pipeline run.

From the tool's definition Tool name 'get_pipeline_run' and description 'Get details of a specific DLT pipeline run' indicate retrieval of run metadata without modification or execution of operations.

Documented attack patterns abuse exactly the kind of access get_pipeline_run gives an agent:

POLICY

How to control get_pipeline_run

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 get_pipeline_run:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "get_pipeline_run": {}
  }
}

get_pipeline_run is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.

Create a free account and register Databricks MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

CAP THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

More Databricks MCP Server tools

Destructive delete_dbfs_path Delete a file or directory from DBFS. Destructive delete_governance_rule Delete a governance rule. Destructive delete_job Delete a job from the Databricks workspace. Destructive delete_pipeline Delete a DLT pipeline. Destructive delete_sql_warehouse Delete a SQL warehouse. Execute cancel_job_run Cancel a running job run. Execute cancel_query Cancel a running query. Execute cancel_statement Cancel statement execution.

All 86 Databricks MCP Server tools →

Read tools on other servers

M-Team MCP Server get_torrent_detail YouTube MCP Server search_channel Android Forensics ADB MCP Server parse_browser_history 0xarchive web3_challenge

Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Data exfiltration → How read access becomes an exfiltration channel when chained with untrusted content.
MCP token cost → What connecting this server costs in context-window tokens on every request.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about get_pipeline_run

What does the get_pipeline_run tool do? +

Get details of a specific DLT pipeline run. It is categorised as a Read tool in the Databricks MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on get_pipeline_run? +

Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for get_pipeline_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 get_pipeline_run? +

get_pipeline_run is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit get_pipeline_run? +

Yes. Add a rate_limit block to the get_pipeline_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 get_pipeline_run completely? +

Set action: deny in the PolicyLayer policy for get_pipeline_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 get_pipeline_run? +

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

Enforce policy on every Databricks MCP Server tool call.

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.

CHECK YOUR STACK →