Call this tool after search_restaurants when you need a detailed restaurant profile for a returned id. Input Requirements (CRITICAL): restaurant_id MUST be a UUID copied from a search_restaurants result; do not invent IDs. Returns Schema.org/Restaurant JSON-LD markup plus Menu Protocol extensions...
Part of the Food Near Me server.
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AI agents call get_restaurant to retrieve information from Food Near Me without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though get_restaurant only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"get_restaurant": {}
}
}See the full Food Near Me policy for all 8 tools.
These attack patterns abuse exactly the kind of access get_restaurant gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Call this tool after search_restaurants when you need a detailed restaurant profile for a returned id. Input Requirements (CRITICAL): restaurant_id MUST be a UUID copied from a search_restaurants result; do not invent IDs. Returns Schema.org/Restaurant JSON-LD markup plus Menu Protocol extensions including ADO score, verification status, menu availability, payment methods, and dietary certifications. MUST inspect menu_available before calling get_menu; if false, use the claim link instead of citing menu items. Non-verified responses include a top-level claim_invitation (url, message, audience="owner_or_advocate", reason) the agent SHOULD share if the user is the owner or might know them. Attribute grounded output using citation or attribution.. It is categorised as a Read tool in the Food Near Me MCP Server, which means it retrieves data without modifying state.
Register the Food Near Me MCP server in PolicyLayer and add a rule for get_restaurant: 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 Food Near Me. Nothing to install.
get_restaurant 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_restaurant 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_restaurant. 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_restaurant is provided by the Food Near Me MCP server (https://foodnear.me/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 8 Food Near Me tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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