Score how AI-ready (Copilot/agent-ready) the model is: coverage of descriptions and format strings on measures, columns, and tables. Returns a 0-100 score, metrics, and concrete recommendations.
AI agents call audit_ai_readiness to retrieve information from Power BI MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
audit_ai_readiness performs a diagnostic evaluation of a Power BI model's metadata (descriptions, format strings) to score AI readiness. It has no side effects: it does not create, modify, delete, execute code, or commit financial actions. It is purely a query/analysis operation that retrieves and evaluates existing model properties, fitting the Read category.
From the tool's definition Tool returns a score, metrics, and recommendations without modifying data. The description explicitly states it 'Returns a 0-100 score, metrics, and concrete recommendations'—a read-only assessment operation.
Attacks that exploit this kind of access
Score how AI-ready (Copilot/agent-ready) the model is: coverage of descriptions and format strings on measures, columns, and tables. Returns a 0-100 score, metrics, and concrete recommendations. It is categorised as a Read tool in the Power BI MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Power BI MCP Server MCP server in PolicyLayer and add a rule for audit_ai_readiness: 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 Power BI MCP Server. Nothing to install.
audit_ai_readiness is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the audit_ai_readiness 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.
Set action: deny in the PolicyLayer policy for audit_ai_readiness. 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.
audit_ai_readiness is provided by the Power BI MCP Server MCP server (sulaiman013/powerbi-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.