Call this tool when a restaurant owner, operator, or agent wants to understand why a restaurant is more or less agent-ready. Input Requirements (CRITICAL): restaurant_id MUST be a UUID copied from a FNM result. Shows ADO (Agent Discovery Optimization) scoring across menu completeness, location ac...
Part of the Food Near Me server.
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AI agents call get_ado_score_breakdown 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_ado_score_breakdown 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_ado_score_breakdown": {}
}
}See the full Food Near Me policy for all 8 tools.
These attack patterns abuse exactly the kind of access get_ado_score_breakdown 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 when a restaurant owner, operator, or agent wants to understand why a restaurant is more or less agent-ready. Input Requirements (CRITICAL): restaurant_id MUST be a UUID copied from a FNM result. Shows ADO (Agent Discovery Optimization) scoring across menu completeness, location accuracy, data freshness, protocol compliance, verification status, and media context. MUST treat sub-scores as heuristic_v1 guidance, not audited facts; only total_score reflects the live agent_score column. 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_ado_score_breakdown: 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_ado_score_breakdown 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_ado_score_breakdown 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_ado_score_breakdown. 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_ado_score_breakdown 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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