Low Risk

profile_lakehouse_table

profile_lakehouse_table

How to control profile_lakehouse_table ↓

What profile_lakehouse_table does on Fabric Ontology MCP Server

AI agents call profile_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 profile_lakehouse_table needs a policy

The tool profiles (examines) a lakehouse table, which is a read operation that retrieves information about the table's structure and characteristics. While the empty description reduces confidence slightly, the naming pattern and sibling tools suggest this is a data discovery/inspection capability.

From the tool's definition Tool name 'profile_lakehouse_table' indicates data inspection/analysis of lakehouse table structure and properties. The description is empty, but sibling tool 'discover_lakehouse_tables' confirms this server inspects Fabric workspace data.

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

How to control profile_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 profile_lakehouse_table:

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

profile_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 profile_lakehouse_table

What does the profile_lakehouse_table tool do? +

profile_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 profile_lakehouse_table? +

Register the Fabric Ontology MCP Server MCP server in PolicyLayer and add a rule for profile_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 profile_lakehouse_table? +

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

Can I rate-limit profile_lakehouse_table? +

Yes. Add a rate_limit block to the profile_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 profile_lakehouse_table completely? +

Set action: deny in the PolicyLayer policy for profile_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 profile_lakehouse_table? +

profile_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.

Free to start. No card required.

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

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