ワットク（Wattoku）

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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context: estimates are approximate and may differ due to fuel adjustment, usage, and discount conditions; final terms must be verified on official websites; and affiliate compensation is disclosed. This goes well beyond the annotations without contradicting them.

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 description is well-structured: purpose in the first sentence, usage instruction in the second, and necessary disclaimers in a separate paragraph. Every sentence earns its place; the disclaimer could be slightly tighter but is legally and contextually necessary.

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?

Without an output schema, the description lists the key aspects covered by the comparison, supplies usage prerequisites, and includes accuracy caveats and affiliate disclosure. This gives an agent sufficient understanding of the tool's functionality and limitations.

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?

Schema description coverage is 100%, so the baseline is 3. The description repeats the plan_ids source instruction already present in the schema but offers no new parameter semantics. All parameters are thoroughly described in the schema with defaults, enums, and constraints.

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 uses the specific verb '詳細比較します' (compares in detail) and enumerates the comparison dimensions: annual estimate, cancellation fees, fuel cost adjustment method, point rewards, and set discounts. This clearly differentiates it from siblings like search_plans and simulate_cost, establishing a unique purpose.

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 description explicitly instructs that plan IDs should come from search_plans/simulate_cost results, providing a clear workflow. It does not explicitly state when not to use this tool versus alternatives, but the multi-plan comparison scope implicitly distinguishes it from single-plan simulation tools.

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

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

Beyond annotations (readOnlyHint false, destructiveHint false, idempotentHint true), the description adds meaningful behavioral context: the tool only issues a link and does not perform the application itself. It also discloses that estimates are approximate, final terms are on official sites, and the service may receive affiliate compensation. This goes beyond the basic annotation data.

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 main purpose is stated in the first sentence, and the rest is a disclaimer paragraph. The description is reasonably concise, though the disclaimer is somewhat lengthy. It maintains a clear structure with the action first, followed by caveats.

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?

With no output schema, the description should clarify what the tool returns, but it only says 'リンクを発行します' without specifying the format, expiration, or other response details. It also omits prerequisites such as requiring a selected plan. While the tool is simple, the description lacks some completeness regarding the output and context.

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 input schema covers plan_id with description '申込リンクを発行するプランID', providing 100% schema description coverage. The tool description adds no additional parameter-specific meaning beyond what the schema already states, so a baseline score of 3 is appropriate.

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 states '選んだプランの申込ページへのリンクを発行します' which clearly identifies the action (issuing a link) and the resource (application page for the selected plan). It is distinct from sibling tools like compare_plans or search_plans, which focus on comparison and search rather than link issuance.

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 description implies usage by explaining that the user must complete the application on the power company's website, and that the service does not act as an agent. However, it does not explicitly state when to use this tool versus alternatives or mention any exclusions. The usage context is implicit rather than clearly defined.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable context: results are sorted by estimated cost, estimates are approximate, users should verify with official sites, and the service may receive affiliate compensation. This goes beyond the annotation-provided safety profile.

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 description is front-loaded with the core purpose in the first sentence, followed by necessary caveats and affiliate disclosure. While somewhat lengthy, every sentence serves a purpose (estimating accuracy, verification, legal disclosure), so structure is justified.

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?

The description covers purpose, input criteria, output ordering, and important caveats. There is no output schema, so the lack of return format detail is acceptable. It could mention edge cases like no matching plans, but overall the tool's behavior is adequately described for an agent.

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 input schema has 100% parameter coverage with detailed descriptions, so the baseline is 3. The description adds a key semantic connection by noting that household size and monthly kWh are alternative inputs ('または'), which is not specified in the schema. This clarifies the relationship between parameters.

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?

Description uses specific verb '検索' with a clear resource ('家庭向け電気料金プランの候補') and specifies input criteria (area, amperage, household size/kWh) and output ordering (ascending estimated cost). This clearly distinguishes it from siblings like compare_plans, get_switch_link, and simulate_cost.

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 description clearly implies the usage context (searching for electricity plans with cost estimates) and explains which inputs are relevant. However, it does not explicitly mention when not to use this tool or mention alternatives, so it lacks exclusions.

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

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint false), the description discloses that the calculation is approximate, may differ due to fuel cost adjustments and discount conditions, and that the service participates in an affiliate program. This adds valuable context about result reliability and potential bias.

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 description is efficiently structured: the first sentence states purpose, the second gives usage guidance, and the disclaimer is concise and necessary. It front-loads the core function and contains no redundant information.

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 tool with 9 parameters and no output schema, the description explains the main workflow and caveats. It could mention the output structure more explicitly, but the read-only annotation and clear purpose make it sufficiently complete. The auto-selection of plan_ids when omitted is covered in the schema.

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?

With schema description coverage at 89%, the schema already documents most parameters. The description adds the key semantic relationship: monthly_kwh and current_monthly_bill_yen are alternative inputs, and plan_ids originates from search_plans. This clarifies selection logic not present in the schema.

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 clearly states the tool's function: calculating the annual cost of each plan and the difference from the current contract, using either monthly kWh or back-calculation from the monthly bill. It uses a specific verb '試算する' and resource, distinguishing it from sibling tools like compare_plans and search_plans.

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 description provides clear parameter guidance (use monthly_kwh if usage is known, current_monthly_bill_yen otherwise), but it does not explicitly say when to use this tool versus alternatives like compare_plans or get_switch_link. Usage context is implied rather than contrasted with siblings.

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