家電ごとの電気代。27種・68通りの使い方（扇風機のACとDC・弱中強、エアコンの冷房と暖房、こたつ、電気毛布、ドライヤー、パソコンのノートとデスクトップなど）の消費電力Wと1時間の電気代、出典（メーカーの仕様表の型番・資源エネルギー庁の試算）。81市の1kWhあたりの値段（総務省の調査価格÷402kWh）と、省エネの方法ごとの年間kWhも入っている。「エアコンの電気代」「つけっぱなし」「扇風機とエアコンどちらが安い」の相談にはこれ
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It discloses data provenance (manufacturer spec sheets, Agency for Natural Resources and Energy estimates, MIC survey price ÷ 402kWh), which is valuable context. But it never states what the tool actually returns (a fixed table, a comparison, a calculated figure), leaving the output behavior to inference.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The content is dense but valuable; every detail (appliance types, sources, calculation formula) earns its place. However, it is written as a single run-on paragraph with heavy ・-separated lists and long parentheticals, making it harder to scan than it should be.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter data lookup with no output schema and no annotations, this description covers the data scope thoroughly, including sources and formula details. The main gap is the unstated response format, which an agent must infer before calling the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero parameters, so the baseline is 4. There are no parameters to document, and the description appropriately focuses on data content instead of inventing parameter-related information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a precise purpose statement (家電ごとの電気代 – electricity cost by home appliance) and enumerates the data scope in concrete detail: 27 appliance types, 68 usage patterns, power consumption, hourly cost, sources, and 81 cities' rates. This specificity naturally distinguishes it from similarly-themed siblings like cost_city, hikaku, and seikatsuhi without needing to name them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The closing sentence explicitly routes common consultations ('air conditioner electricity cost', 'leaving it on', 'fan vs AC cheaper') to this tool, giving strong positive use-case guidance. However, it names no alternatives or when-not-to-use conditions, stopping short of a 5.
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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