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 does reveal some behavioral traits: it mentions credit consumption (via remainingCredits in returns), default values for parameters, and that the tool generates downloadable audio files. However, it lacks critical information such as rate limits, authentication requirements, whether the generated files are stored permanently, error conditions, or performance characteristics. The description adds value but leaves significant gaps.

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 with clear sections for Args and Returns, making it easy to parse. Each parameter explanation is concise yet informative. The opening statement is direct. While efficient, the Japanese text might require translation for some agents, and the Returns section could be slightly more detailed about response structure, but overall it's appropriately sized with minimal waste.

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 (6 parameters, voice generation with credit consumption) and the absence of both annotations and output schema, the description does a decent job but has notable gaps. It explains parameters thoroughly and outlines the return structure, which helps compensate for the missing output schema. However, it lacks information about error handling, authentication, rate limits, and the relationship with sibling tools, making it incomplete for safe and effective use by an AI 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 description provides excellent parameter semantics beyond the input schema, which has 0% description coverage. For all 6 parameters, it explains their purpose, constraints (e.g., script max 3000 characters, speed range 0.4-3.0), default values, and special meanings (e.g., -1 for emotional_level means use voice actor's default). This fully compensates for the schema's lack of descriptions and adds meaningful context that the schema alone doesn't provide.

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 as '音声を生成してその結果を返す' (generate voice and return the result), which is a specific verb+resource combination. It distinguishes itself from sibling tools like get_credit_balance and get_voice_actors by focusing on voice generation rather than querying resources. However, it doesn't explicitly contrast with potential alternative generation tools beyond the siblings listed.

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 no guidance on when to use this tool versus alternatives. While it mentions that actor_id defaults to the first available actor if omitted, it doesn't explain when to specify an actor_id versus using the default, or how this tool relates to the sibling tools (e.g., whether get_voice_actors should be called first to select an actor). There's no mention of prerequisites, constraints, or typical use cases.

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 only states what the tool does ('クレジット残高を取得') and what it returns ('クレジット残高'), but lacks details on permissions, rate limits, error conditions, or whether it's a read-only operation. For a tool with zero annotation coverage, this is insufficient behavioral context.

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 brief and front-loaded with the main purpose, but the structure could be improved. The 'Returns:' section is redundant since it merely repeats 'クレジット残高' from the first line without adding value. A single concise sentence would be more 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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose and return value, but lacks context about the credit system (e.g., what credits are for, units, or typical values). For a basic read operation, this is acceptable but not comprehensive.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and return value. This meets the baseline expectation for a parameterless tool.

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: 'クレジット残高を取得' (Get credit balance). It uses a specific verb ('取得' - get/retrieve) and resource ('クレジット残高' - credit balance), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'generate_voice' or 'get_voice_actors', which appear to serve different purposes related to voice generation rather than credit management.

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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for usage, or comparisons with sibling tools. The agent must infer usage based solely on the tool's name and description without explicit instructions.

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 only states that it returns a list with details, but does not cover important aspects like whether it's a read-only operation, potential rate limits, authentication needs, or error conditions. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 concise with two sentences, but it is not optimally structured. The first sentence states the purpose, and the second describes the return, but it could be more front-loaded with key details. While not wasteful, it lacks emphasis on critical information like behavioral traits, given the absence of annotations.

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 (a list retrieval tool), lack of annotations, and no output schema, the description is incomplete. It mentions the return type but does not specify the structure of 'details' (e.g., names, IDs, availability), which is crucial for the agent to understand the output. This gap makes it inadequate 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?

The input schema has 0 parameters with 100% coverage, so no parameters need documentation. The description appropriately does not discuss parameters, which is efficient. However, it could have mentioned any implicit parameters or context, but since none exist, it meets the baseline for this dimension.

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: 'Get the list of voice actors.' This is a specific verb ('Get') and resource ('voice actors'), making it understandable. However, it does not explicitly differentiate from sibling tools like 'get_credit_balance' or 'generate_voice', which could involve voice actors in different contexts, so it lacks sibling differentiation.

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 no guidance on when to use this tool versus alternatives. It does not mention sibling tools or contexts where this tool is preferred, such as for listing available actors before generating voice, nor does it specify any prerequisites or exclusions. This leaves the agent without clear usage instructions.

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