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

Annotations already establish readOnly, idempotent, non-destructive behavior. The description adds valuable context beyond that: the data source (MHLW), the annual October revision, the returned fields (latest rate, previous rate, increase, effective date), and the warning that LLM memory is stale. This is meaningful behavioral disclosure.

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?

Two focused sentences: the first states the core purpose and scope, the second adds official source, revision timing, return contents, and the stale-memory warning. Every sentence earns its place with no filler.

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 one optional parameter, complete schema coverage, rich annotations, and an output schema present, the description covers everything else an agent needs: what the tool returns, when data updates, and why the tool should be trusted over the model's own knowledge.

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 only parameter, 'prefecture', has 100% schema description coverage with format examples, default, and the '全国/nationwide' special case. The tool description itself adds little parameter-specific meaning, but the schema already carries the full burden, so the baseline of 3 applies.

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 a specific verb ('Get'), a specific resource ('regional minimum wage for a Japanese prefecture'), and clarifies it returns the hourly rate. It also distinguishes itself from sibling tools by emphasizing 'current' figures versus history, ranking, or prefecture listing.

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 context for when to use this tool: when you need the current official minimum wage for a prefecture, explicitly noting the October revision cycle and that LLM memory is stale by design. It does not explicitly say 'use get_minimum_wage_history for historical data' or 'use rank_minimum_wages for comparisons,' so exclusions are only implied.

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 readOnlyHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds meaningful context by noting the official MHLW data source, the time range back to FY2002, and the return structure (per-fiscal-year amount, effective date, and min/max). This goes beyond the annotations and helps the agent understand what to expect.

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 two sentences with no fluff. The main purpose is front-loaded, followed by a concise note on data availability and return content. Every sentence adds value, making it well-structured and efficient.

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?

Given the tool's moderate complexity (2 optional params, defaults, and an output schema), the description is complete. It explains what is returned (per-year amount, effective date, min/max) and notes the data range, which is sufficient for an agent to understand the tool's behavior. The output schema exists, so the description need not detail return fields further.

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 schema already documents both parameters (years and prefecture). The description adds no new meaning to the parameters beyond what the schema states, such as the meaning of 'fiscal years' which is already in the schema. Thus the baseline of 3 applies, as the description doesn't need to compensate.

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 retrieves the revision history of a prefecture's minimum wage over recent fiscal years, with a specific verb and resource. It distinguishes itself from siblings like get_minimum_wage (likely current wage) and rank_minimum_wages (ranking), making its unique purpose evident.

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 context that this tool is for historical revision data, which implies when to use it versus alternatives like get_minimum_wage. However, it does not explicitly exclude other scenarios or name alternatives, so it lacks explicit when-not guidance. The context is clear but not exhaustive.

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 readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral details beyond annotations: the exact fiscal-year range, that the tool is intended as a first call, and what the response contains, including the national weighted average.

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 compact and front-loaded with the core purpose, followed by a clear usage directive and a concise return summary. Every sentence adds value; there is no filler or repetition of structured 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?

Given zero parameters, a present output schema, and rich annotations, the description is complete. It tells the agent what the tool returns, when to call it, and the scope of data covered, leaving no important gap for correct invocation.

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 tool has zero parameters and an empty input schema, so there is nothing for the description to explain about parameters. The schema coverage is 100% trivially, and the description correctly focuses on return content rather than parameter semantics.

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 a specific verb and resource: 'List all 47 prefectures with romaji aliases and the available fiscal years.' This clearly distinguishes it from the sibling tools, which retrieve minimum-wage data rather than enumerate prefectures and fiscal coverage.

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?

It gives explicit context: 'Call this first if unsure which prefecture names are valid, or to discover the historical coverage.' It does not explicitly enumerate when not to use it or name alternatives, but the usage context is clear enough for an agent to select it as a discovery/prerequisite tool.

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 cover read-only, idempotent, and non-destructive behavior, so the bar for additional disclosure is lower. The description adds that the national weighted average is included and specifies the ordering ('cheapest first'), which is useful. However, the phrase 'plus most expensive' is ambiguous—it could mean the ranking includes both ends or simply that the most expensive is at the end. No contradiction with annotations, but the behavioral disclosure is minimal and slightly unclear.

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 compact and front-loaded with the core purpose, followed by use cases and a note about the national average. No redundant sentences; every line earns its place. It is well-structured for quick agent parsing.

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?

Given the tool's simplicity (one optional parameter, output schema present), the description covers the essential aspects: what it ranks, ordering, and the inclusion of the national average. It does not explicitly describe the output format, but the output schema handles that. It could mention limit behavior explicitly, but the schema already does. Overall complete enough for an agent to call correctly.

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%, with a single parameter 'limit' fully described in the schema (default, max, purpose). The description adds no additional parameter semantics, which is acceptable given the high coverage. Baseline of 3 applies.

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: ranking Japan's 47 prefectures by current minimum wage, with an explicit ordering ('cheapest first') and inclusion of the most expensive. This is a specific verb+resource that distinguishes it from sibling tools like get_minimum_wage (single lookup), get_minimum_wage_history (historical data), and list_prefectures (just listing). No ambiguity.

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 use cases ('cost-of-labor comparisons, relocation planning, and HR budgeting') that signal when this tool is appropriate. However, it does not explicitly contrast with sibling tools or state when not to use it. The context is helpful but lacks explicit exclusions or alternative routing.

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