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AIDISH — AIエージェントから受取注文できる飲食店

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店のお品書き(公開中の商品・税込価格・説明・オプション)を返す。注文の前に必ずこれで menu_id を確かめる。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tenant_idNo店舗ID(省略時はこのサーバーの既定店)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full disclosure burden. It usefully reveals scope (only publicly visible items, tax-included pricing) and implies that menu_id values are the key output needed downstream, but says nothing about pagination, result size, auth/tenant resolution, or limits.

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

Conciseness5/5

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

Two short sentences: one declaring the returned content, one stating the ordering precondition. Nothing is padded, and the precondition is placed after the payload so the reader gets capability first, then action.

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?

With no output schema, the description must convey return content, and it does (products, prices, descriptions, options) plus the downstream menu_id purpose. It still omits how tenant scoping affects results and whether the list is complete or paginated, which are minor gaps for a simple read tool.

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?

The single tenant_id parameter is fully documented in the schema (店舗ID, defaulting to the server's default store), and the description adds no additional meaning about it. With schema coverage at 100%, the schema does the work and a baseline 3 is appropriate.

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?

States a specific verb and resource ('returns the store's menu') and enumerates the payload (publicly available products, tax-included prices, descriptions, options), so an agent knows exactly what it fetches. It does not, however, explicitly contrast itself with siblings like find_stores or place_pickup_order, so differentiation is left to the workflow hint rather than stated in the purpose itself.

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

Usage Guidelines4/5

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

Gives an explicit precondition tied to the ordering workflow: 'before ordering, always verify menu_id with this,' which effectively routes the agent here ahead of place_pickup_order. It stops short of naming the alternative tool or any when-not conditions, so it is clear context without full exclusion guidance.

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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