AI agents call get_venue to retrieve information from Palate without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and returns data about a venue (details, reviews, scores, signals) without creating, modifying, executing operations, or deleting anything. It is a read-only retrieval operation with minimal risk even if misused by an agent, as the worst case is unauthorized information disclosure about public venue data.
From the tool's definition Tool name 'get_venue' and description 'Get detailed info for a specific venue, including all reviews, scores, and signals' — retrieves venue information with no modification or deletion.
Attacks that exploit this kind of access
Get detailed info for a specific venue, including all reviews, scores, and signals. It is categorised as a Read tool in the Palate MCP Server, which means it retrieves data without modifying state.
Register the Palate MCP server in PolicyLayer and add a rule for get_venue: 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 Palate. Nothing to install.
get_venue 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 get_venue 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 get_venue. 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.
get_venue is provided by the Palate MCP server (palate-mcp-server). 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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