Get shopping/product queries that AI models generated when answering prompts. Returns product-related queries with associated product names. Useful for understanding product recommendations by AI models. Without date filters, returns data across all available dates. Empty results may indicate the...
AI agents call shopping_queries to retrieve information from Peecai without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and queries analytics data about shopping queries generated by AI models. It has no side effects—it only reads and returns existing data. The sibling tools on the server include destructive operations (delete_*), but this specific tool is purely a read/query operation.
From the tool's definition Tool description states it 'Get[s] shopping/product queries' and 'Returns product-related queries', indicating data retrieval with no modification capabilities.
Attacks that exploit this kind of access
Get shopping/product queries that AI models generated when answering prompts. Returns product-related queries with associated product names. Useful for understanding product recommendations by AI models. Without date filters, returns data across all available dates. Empty results may indicate the project has no query data for the given time range or filters. It is categorised as a Read tool in the Peecai MCP Server, which means it retrieves data without modifying state.
Register the Peecai MCP server in PolicyLayer and add a rule for shopping_queries: 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 Peecai. Nothing to install.
shopping_queries 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 shopping_queries 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 shopping_queries. 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.
shopping_queries is provided by the Peecai MCP server (mcp-server-peecai). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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