Low Risk

preview_lakehouse_table

preview_lakehouse_table

How to control preview_lakehouse_table ↓

What preview_lakehouse_table does on Fabric Ontology MCP Server

AI agents call preview_lakehouse_table to retrieve information from Fabric Ontology MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

Low Risk

Why preview_lakehouse_table needs a policy

The tool name uses 'preview' which semantically indicates a read-only operation to view or query table data. Although the description is empty, the context of sibling discovery tools (discover_lakehouse_tables, discover_workspace_data) suggests this follows a query-then-preview pattern typical of data exploration. There are no indicators of code execution, data modification, deletion, or financial operations.

From the tool's definition Tool name 'preview_lakehouse_table' indicates a read operation that retrieves or displays table data from a lakehouse without modification.

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

How to control preview_lakehouse_table

PolicyLayer is an MCP gateway — it sits between your AI agents and Fabric Ontology MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for preview_lakehouse_table:

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "preview_lakehouse_table": {}
  }
}

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

  1. Create a free account and register Fabric Ontology MCP Server — nothing to install.
  2. Add this policy — paste it, or build it visually.
  3. Point your MCP client (Claude, Cursor, anything) at your gateway URL.
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Related tools and policies

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Questions about preview_lakehouse_table

What does the preview_lakehouse_table tool do? +

preview_lakehouse_table. It is categorised as a Read tool in the Fabric Ontology MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on preview_lakehouse_table? +

Register the Fabric Ontology MCP Server MCP server in PolicyLayer and add a rule for preview_lakehouse_table: 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 Fabric Ontology MCP Server. Nothing to install.

What risk level is preview_lakehouse_table? +

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

Can I rate-limit preview_lakehouse_table? +

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

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

preview_lakehouse_table is provided by the Fabric Ontology MCP Server MCP server (tmdaidevs/ontology-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Fabric Ontology MCP Server tool call.

Start from Fabric Ontology MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

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45 Fabric Ontology MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

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