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sacloud

sacloud-mcp

Official
by sacloud

get_price

Retrieve pricing information for Sakura Cloud services, including hourly, daily, and monthly rates for different service classes and zones.

Instructions

さくらのクラウドの利用料金を取得します。 Returns: dict: さくらのクラウドの利用料金情報 - Count (int): 取得したサービスクラスの総数 - ResponsedAt (str): レスポンス生成日時(ISO 8601 形式) - ServiceClasses (dict): - <キー> (dict): 各サービスクラスの詳細情報 - DisplayName (str): プランの表示名 - IsPublic (bool): 公開フラグ - Price (dict): - Daily (int): 日額(円) - Hourly (int): 時額(円) - Monthly (int): 月額(円) - Zone (str): ゾーン名 - ServiceCharge (str): 課金種別 - ServiceClassID (int): サービスクラス ID - ServiceClassName (str): サービスクラス名(内部名称) - ServiceClassPath (str): サービスクラスのパス

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states it 'gets' prices, implying a read-only operation, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or whether it fetches real-time or cached data. The description focuses on the return format but lacks operational context.

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 description is front-loaded with the purpose in the first sentence, but the detailed return format (占쏙옙 of the text) might be excessive for a description, especially without an output schema. It could be more concise by summarizing the return structure or omitting it if not critical for tool selection. However, it's not overly verbose.

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

Completeness3/5

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

Given the tool has 0 parameters, no annotations, and no output schema, the description provides the purpose and detailed return format. This is adequate for a simple read operation, but it lacks context on usage guidelines and behavioral transparency, which are important for an AI agent to invoke it correctly in a broader toolset.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter semantics, but that's acceptable here. A baseline of 4 is appropriate as there are no parameters to explain, and the schema fully covers this aspect.

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 clearly states the tool's purpose: 'さくらのクラウドの利用料金を取得します' (Get usage fees for Sakura Cloud). It specifies the verb '取得します' (get) and resource '利用料金' (usage fees), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'get_bill_list' or 'get_coupon_list', which might also relate to billing/costs.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_bill_list' (which might list invoices) or 'get_coupon_list' (which might list discounts), nor does it specify prerequisites or contexts for usage. The agent must infer usage from the tool name and description alone.

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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