query_lakehouse_sql_endpoint · Semantic Model MCP Server MCP

WHAT IT DOES

What query_lakehouse_sql_endpoint does on Semantic Model MCP Server

AI agents invoke query_lakehouse_sql_endpoint to trigger actions in Semantic Model 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.

WHY IT NEEDS A POLICY

Why query_lakehouse_sql_endpoint needs a policy

The tool name strongly implies executing SQL queries against a lakehouse SQL endpoint. Given the server context (Microsoft Fabric, Power BI semantic models) and sibling tools like 'execute_dax_query', this tool likely runs SQL queries that could have read or execute-level effects. Since SQL queries can range from SELECT to DDL/DML operations, Execute is the most appropriate category.

From the tool's definition Tool name contains 'query' and 'sql_endpoint', suggesting SQL execution against a lakehouse endpoint. Description is empty and uninformative.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

query_lakehouse_sql_endpoint Rate-limited

query_lakehouse_sql_endpoint 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": {
    "query_lakehouse_sql_endpoint": {
      "limits": [
        {
          "counter": "query_lakehouse_sql_endpoint_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

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

FAQ

Questions about query_lakehouse_sql_endpoint

What does the query_lakehouse_sql_endpoint tool do? +

query_lakehouse_sql_endpoint. It is categorised as a Execute tool in the Semantic Model 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 query_lakehouse_sql_endpoint? +

Register the Semantic Model MCP Server MCP server in PolicyLayer and add a rule for query_lakehouse_sql_endpoint: 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 Semantic Model MCP Server. Nothing to install.

What risk level is query_lakehouse_sql_endpoint? +

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

Can I rate-limit query_lakehouse_sql_endpoint? +

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

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

query_lakehouse_sql_endpoint is provided by the Semantic Model MCP Server MCP server (nahtheking/semantic-model-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.