lakehouse_optimization_recommendations

THE RISK

Low Risk

Why lakehouse_optimization_recommendations needs a policy

This tool retrieves and analyzes configuration and health metrics of Lakehouse items to provide optimization recommendations. It performs diagnostic scanning to detect performance bottlenecks and configuration issues, which are read-only operations with no side effects on the underlying data or system state.

From the tool's definition Tool name 'lakehouse_optimization_recommendations' and description 'LIVE SCAN: Analyzes a Fabric Lakehouse by checking table formats' indicates the tool performs analysis and scanning operations without modifying data.

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

POLICY

How to control lakehouse_optimization_recommendations

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

policy.json

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

lakehouse_optimization_recommendations 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 Force Fabric 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.

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EXPLORE

Related tools and policies

More Force Fabric MCP Server tools

Destructive auth_logout Logout from Microsoft Fabric and clear cached credentials. Execute lakehouse_run_table_maintenance Run table maintenance (OPTIMIZE with V-Order, Z-ORDER, VACUUM) on a Fabric Lakehouse. Execute eventhouse_auto_optimize AUTO-OPTIMIZE: Scans a Fabric Eventhouse for all fixable issues across all KQL databases a Execute eventhouse_fix AUTO-FIX: Applies fixes to a Fabric Eventhouse. Execute eventhouse_fix_materialized_views AUTO-FIX: Diagnoses and repairs broken materialized views in a Fabric Eventhouse. Execute gateway_fix AUTO-FIX: Applies fixes to gateway and connection issues. Execute lakehouse_auto_optimize AUTO-OPTIMIZE: Discovers ALL Delta tables in a Lakehouse and applies fixes to every table Execute lakehouse_fix AUTO-FIX: Applies Spark SQL fixes to a Lakehouse via Livy API (no notebooks needed).

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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 lakehouse_optimization_recommendations

What does the lakehouse_optimization_recommendations tool do? +

LIVE SCAN: Analyzes a Fabric Lakehouse by checking table formats (Delta vs non-Delta),. It is categorised as a Read tool in the Force Fabric MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on lakehouse_optimization_recommendations? +

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

What risk level is lakehouse_optimization_recommendations? +

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

Can I rate-limit lakehouse_optimization_recommendations? +

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

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

lakehouse_optimization_recommendations is provided by the Force Fabric MCP Server MCP server (tmdaidevs/force-fabric-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 Force Fabric MCP Server tool call.

Start from Force Fabric 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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