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ChefBear

Save a menu

save_menu
Idempotent

Use this when the user asks to save a restaurant menu to their ChefBear account and you can already read its dishes, for example from a menu photo or text in the conversation. Saves the dishes you pass as a private ChefBear menu at once, without running recognition and without using the user's recognition allowance. Copy each dish name exactly as printed (name), with its price and currency when shown; add English and Chinese names and short descriptions only when you are sure of them. Returns the menuId and a private link to open it in ChefBear. Don't call it unless the user wants the menu saved; use import_menu instead when they want ChefBear itself to recognize the photos.

Input Schema

NameRequiredDescriptionDefault
itemsYesEvery dish on the menu, in menu order.
titleYesThe restaurant's name as shown on the menu, or a short description such as "Thai lunch menu".
languageNoThe user's language for this menu: en, zh-Hans, zh-Hant, es, vi, ko or ja. When omitted, ChefBear uses the conversation's language if the app reports one of these, else en.
requestIdYesA new unique ID for this save, such as a UUID. Repeating it with the same menu returns the same saved menu instead of saving twice.
restaurantTypeNoThe kind of restaurant in one or two words, such as thai, sushi or cafe.

Output Schema

NameRequiredDescriptionDefault
titleYes
menuIdYes
menuUrlYes
dishCountYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare non-read-only, idempotent, non-destructive, closed-world, but the description adds real context they don't carry: the save runs no recognition, consumes none of the recognition allowance, produces a private menu, and returns a menuId plus a private link. It does not discuss failure modes, partial saves, or limits, which keeps it off a 5.

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

Conciseness4/5

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

Front-loads the trigger condition, then behavioral effects, then the sibling exclusion — a logical order with no filler. It is on the long side for one tool, but each clause (allowance savings, exact-name fidelity, import_menu routing) carries distinct operational value.

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

Completeness5/5

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

Covers the full decision surface for a mutation tool: when to invoke, when not to, the alternative, what side effects occur, and how to shape nested dish data. Since an output schema exists, the brief mention of menuId and the private link is sufficient rather than redundant.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3; the description still adds genuine data-quality rules beyond the schema by telling the caller to copy dish names exactly as printed and to supply English/Chinese names and descriptions only when confident. It says nothing extra about requestId, language, or restaurantType beyond what the schema already documents.

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

Purpose5/5

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

States a specific verb and resource ("save a restaurant menu to their ChefBear account") and scopes the precondition ("you can already read its dishes, for example from a menu photo or text"). It explicitly distinguishes itself from the sibling import_menu, so an agent can route without opening a schema.

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

Usage Guidelines5/5

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

Gives an explicit trigger (user asks to save a menu and dishes are already readable), an explicit prohibition ("Don't call it unless the user wants the menu saved"), and the competing alternative with its selecting condition ("use import_menu instead when they want ChefBear itself to recognize the photos").

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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