Low Risk

get_menu

Call this tool only when a search_restaurants or get_restaurant response has menu_available: true. Input Requirements (CRITICAL): restaurant_id MUST be a UUID copied from a prior FNM result. Returns the full menu in Menu Protocol v1.0 format with item dietary booleans, declared allergens, customi...

Part of the Food Near Me server.

get_menu is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call get_menu 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_menu 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.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "get_menu": {}
  }
}

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Get this rule live on your own Food Near Me server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access get_menu gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so get_menu only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the get_menu tool do? +

Call this tool only when a search_restaurants or get_restaurant response has menu_available: true. Input Requirements (CRITICAL): restaurant_id MUST be a UUID copied from a prior FNM result. Returns the full menu in Menu Protocol v1.0 format with item dietary booleans, declared allergens, customization options, preparation times, trust notice, and signature metadata. PREFER verification_status: "verified" for dietary/allergen answers. On menu_indexed, MUST treat dietary/allergen data as best-effort public indexing and surface item-level caution text to the user; the response also includes a top-level claim_invitation that SHOULD be surfaced when 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.

How do I enforce a policy on get_menu? +

Register the Food Near Me MCP server in PolicyLayer and add a rule for get_menu: 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.

What risk level is get_menu? +

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

Can I rate-limit get_menu? +

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

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

get_menu 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.

Enforce policy on every Food Near Me tool call.

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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