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 discloses key behavioral traits: the tool generates markup with specific cultural features (e.g., field order, furigana, phone format) and adapts keigo based on context. However, it lacks details on output format (implied but not explicit), error handling, or performance aspects like rate limits. The description adds value but is incomplete for a tool with 7 parameters and no output schema.

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 appropriately sized and front-loaded, with the first sentence stating the core purpose and key features, and the second sentence providing usage guidelines. Every sentence earns its place by adding distinct value—no redundancy or waste. It efficiently conveys essential information without unnecessary elaboration.

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 complexity (7 parameters, cultural nuances) and lack of annotations or output schema, the description is reasonably complete. It covers the purpose, key features, and usage context, but could benefit from more behavioral details (e.g., output format clarification, error handling). However, it adequately supports agent understanding for a generation tool with well-documented parameters 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds marginal semantic context by implying how 'context' affects keigo level and 'fields' includes culturally appropriate elements, but it doesn't provide additional syntax or format details beyond what the schema specifies. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't significantly enhance parameter understanding.

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 specific action ('generate culturally correct Japanese form markup') and resource ('Japanese form'), listing key features like field order, furigana, phone format, postal address, dates, and keigo. It explicitly distinguishes this tool from siblings by specifying 'when building any form for a Japanese audience,' which contrasts with tools like 'validate_jp_form' or 'suggest_keigo_level' that serve different purposes.

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 explicit guidance on when to use this tool: 'Use this when building any form for a Japanese audience.' This clearly defines the primary use case and context, helping the agent distinguish it from alternatives like 'generate_jp_placeholder' (likely for placeholder text) or 'transform_for_japan' (likely for conversion tasks). No exclusions are stated, but the positive guidance is sufficient for high clarity.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the output format in detail (e.g., 'kanji + katakana furigana + romaji', 'real postal codes and prefectures'), which is valuable, but does not cover aspects like rate limits, error handling, or data sources. It adequately informs that the tool generates data but lacks deeper behavioral traits.

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 appropriately sized and front-loaded, starting with the core purpose and efficiently listing output types in a single, well-structured sentence. Every phrase adds value without redundancy, making it easy for an agent to quickly grasp the tool's function.

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 (4 parameters, no output schema, no annotations), the description is largely complete. It details the output format comprehensively, which compensates for the lack of output schema. However, it could improve by mentioning constraints like the 'count' maximum or 'fields' enum options, though these are 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?

The input schema has 100% description coverage, providing clear details for all parameters (e.g., 'count', 'fields', 'gender', 'age_range'). The description adds minimal value beyond the schema by mentioning 'Japanese era format' for dates, which relates to output rather than input. Baseline score of 3 is appropriate as the schema does the heavy lifting.

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 purpose with specific verbs ('Generate realistic Japanese test data') and resources ('names, addresses, phone numbers, company names, and dates'), distinguishing it from siblings like 'validate_jp_form' or 'transform_for_japan' by focusing on data generation rather than validation or transformation.

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 context ('for prototypes and development') but does not explicitly state when to use this tool versus alternatives like 'generate_jp_form' or 'suggest_keigo_level'. It lacks guidance on exclusions or prerequisites, leaving the agent to infer appropriate scenarios.

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?

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool analyzes 5 specific categories and returns a breakdown with issues and quick wins. However, it doesn't mention performance characteristics (e.g., processing time), error handling, or authentication requirements that would be helpful for an agent.

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 in two sentences: the first states the core purpose and scope, the second details the 5 analysis categories and return format. Every sentence earns its place with zero wasted words, making it easy for an agent to parse quickly.

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 4-parameter analysis tool with no annotations and no output schema, the description provides good context about what the tool analyzes and returns. It covers the 5 evaluation categories and output structure (breakdown with issues and quick wins). However, without an output schema, more detail about the return format would be beneficial for agent understanding.

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 all 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain markup format requirements or context implications). Baseline 3 is appropriate when the schema does the heavy lifting.

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 purpose: 'Score any page or component for Japan-readiness on a 0-100 scale' with specific verb ('Score') and resource ('page or component'). It distinguishes from siblings by focusing on comprehensive analysis rather than specific generation or validation tasks like 'generate_jp_form' or 'validate_jp_form'.

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 context through the 5 analysis categories (forms, copy, trust, typography, cultural), suggesting it's for evaluating Japanese localization readiness. However, it doesn't explicitly state when to use this tool versus alternatives like 'transform_for_japan' or 'validate_jp_form', nor does it provide exclusion criteria or prerequisites.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'suggests' and 'returns' translations, implying a read-only operation, but does not clarify if it requires authentication, has rate limits, or details the return format (e.g., structured output). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 appropriately sized with two sentences that are front-loaded with the core purpose. It efficiently lists UI elements and contexts without redundancy, though the second sentence could be slightly more streamlined by integrating the examples more cohesively.

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 complexity of language translation and cultural adaptation, the description is moderately complete. It lacks an output schema, so the agent does not know the return format (e.g., whether it includes multiple translations or keigo levels). With no annotations and incomplete behavioral details, it provides a basic understanding but leaves gaps for effective tool 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'maps business context to keigo level' and listing examples like 'buttons, error messages,' which loosely correlate with the ui_element enum. However, it does not provide additional syntax or format details, aligning with the baseline score when schema coverage is high.

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 purpose: 'Suggest appropriately polite Japanese UI text based on context.' It specifies the verb ('suggest'), resource ('Japanese UI text'), and scope ('based on context'), distinguishing it from sibling tools like generate_jp_form or validate_jp_form. The mention of mapping business context to keigo levels and returning translations for specific UI elements adds specificity.

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 mentioning 'based on context' and listing UI elements and business contexts, but it does not explicitly state when to use this tool versus alternatives like generate_jp_form or transform_for_japan. No exclusions or prerequisites are provided, leaving the agent to infer appropriate contexts from the parameter enums.

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?

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool performs multiple transformations, shows before/after scores, and explains changes. However, it doesn't mention error handling, performance characteristics, or whether it's idempotent. The description adds useful context but doesn't fully compensate for the lack of annotations.

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: first sentence states the core purpose, followed by a bullet-like list of specific transformations, ending with output behavior. Every sentence adds value with zero redundant information. It's appropriately sized for a complex transformation tool.

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 complexity (4 parameters, no annotations, no output schema), the description does well by detailing the transformation scope and output behavior (scores and explanations). However, without an output schema, it doesn't fully describe the return format or structure. The description is reasonably complete but could benefit from more detail about the output.

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 adds value by implicitly explaining how parameters relate to the transformation: 'markup' is the input to transform, 'context' determines keigo level and conventions, 'format' specifies output format, and 'preserve_styling' relates to CSS preservation. This provides meaningful context beyond the schema's technical descriptions.

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 transforms Western markup into Japan-ready markup with specific transformations listed (name field order, furigana addition, phone splitting, address restructuring, button translation, placeholder replacement). It distinguishes from siblings by focusing on transformation rather than generation, scoring, or validation.

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 when needing to adapt Western markup for Japanese contexts, but doesn't explicitly state when to use this vs. alternatives like generate_jp_form or score_japan_readiness. It provides clear context but lacks explicit exclusions or comparison to sibling 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?

With no annotations provided, the description carries the full burden. It clearly describes the tool's behavior: it audits forms, checks specific conventions, and returns a score with issues and fixes. It doesn't mention error handling, performance, or authentication needs, but for a validation tool, the disclosed behavior is reasonably comprehensive.

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, followed by specific checks and output details in a single, efficient sentence. Every element (audit action, checks, return values) adds value without redundancy, making it highly concise and well-structured.

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 no annotations and no output schema, the description compensates well by explaining the tool's purpose, checks performed, and return format (score, issues, fixes, passed items). For a validation tool with 2 parameters, this is nearly complete, though it could mention error cases or performance considerations.

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 thoroughly. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain HTML/JSX format requirements or context implications). Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific action ('Audit an existing form against Japanese UX conventions') and enumerates the exact checks performed (name field order, furigana, phone format, postal code, keigo, character handling). It distinguishes from sibling tools like 'generate_jp_form' (which creates forms) and 'score_japan_readiness' (which appears broader).

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 context by specifying 'Japanese UX conventions' and listing specific checks, suggesting it's for forms targeting Japanese users. However, it doesn't explicitly state when to use this versus alternatives like 'score_japan_readiness' or 'suggest_keigo_level', nor does it mention prerequisites or exclusions.

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