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Menu prices

menu_prices
Read-onlyIdempotent

What things cost on restaurant menus in a city: the middle price, and the middle half of prices, of an espresso, a cappuccino, a beer, a glass of wine, a margherita, a burger, a main course, a lunch offer, a cola and a bottle of still water, read from the menus on restaurants', cafes' and bars' own websites, with the number of menus behind each figure, the day they were read and how many prices a hand check found right. Covers 13 European cities so far (Paris, Madrid, Berlin, Milan, Lisbon, Vienna and others); without a city it lists them. Figures are menu prices, not what a bill comes to.

Input Schema

NameRequiredDescriptionDefault
cityNoCity slug, for example paris or madrid; leave out for the list of cities with prices
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
conversation_idNoPass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare it as a safe read (readOnly, idempotent, non-destructive), so the bar is lower. The description adds genuinely useful context beyond the structured fields: data provenance (restaurants' own websites), freshness ('the day they were read'), reliability ('how many prices a hand check found right'), and a scope caveat ('menu prices, not what a bill comes to').

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening clause is well front-loaded, but the body is one dense run-on cramming in ten menu items plus four metadata fields. It's informative but not efficiently structured; the item list could be summarized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the burden of describing returns, and it does so thoroughly: which items, which statistics, what metadata accompanies each figure. Combined with annotations covering the safety profile, an agent has enough to invoke and interpret it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description only echoes the city semantics already in the schema ('Covers 13 European cities... without a city it lists them'). The context/llm_model/conversation_id plumbing params are handled entirely by the schema, so baseline 3 is correct.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource (menu prices for a set of named items in a city) and even spells out the statistics returned (median and interquartile range). It's more specific than most siblings like city_facts or rankings, but it never explicitly distinguishes itself from them or names an alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: you call it to get menu prices, and 'without a city it lists them' documents the parameter-omission behavior. However there is no explicit when-to-use vs alternatives (e.g. compare_cities, city_facts), so the agent must infer the routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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