Text to Speech Voiceover

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 explains the output (download link, duration, file size) but does not mention any side effects, authentication requirements, rate limits, or costs. For a generation tool, these aspects could be relevant, but the core behavior is adequately described.

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, front-loaded with the action, and contains no filler. Every sentence adds value: the first explains the core function, the second lists the return values. It is 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?

The description covers the essential information: what the tool does, the inputs needed, and what it returns. It does not mention errors or edge cases, but for a straightforward TTS generation tool with a thorough schema, this is largely sufficient. The lack of an output schema is compensated by the explicit list of return values.

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 already provides full descriptions for both parameters (100% coverage), including the voice object's required fields. The tool description itself adds no parameter-specific semantics beyond what the schema states, so the baseline 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 uses a specific verb ('Generates') and identifies the resource ('Text to Speech audio voiceover'), clearly stating it converts text to a spoken audio file. This distinguishes it from siblings like get_voices (retrieves voices) and usage_statistic (tracks usage) by focusing on audio generation.

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 the tool is used when you have a voice object and text, but it does not explicitly state when to use it versus alternatives or mention prerequisites. The schema's note about using get_voices is not part of the tool description itself, so usage context is implied rather than explicit.

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 burden. It discloses that the tool is a retrieval operation (non-destructive) and that it returns a list of voices, and emphasizes that obtaining a voice object is a prerequisite for TTS generation. It does not mention potential response size or error behavior, but for a simple list-retrieval tool this is adequate.

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?

Three sentences, each earning its place. The description is front-loaded with the tool's main purpose, followed by the critical prerequisite, then the optional parameter. No unnecessary words or repetition.

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 (only one optional parameter, no output schema, no annotations), the description fully covers the essential information: what it does, when to use it, and how to use the parameter. It also aligns with sibling tool context by establishing the dependency on generate_tts.

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%; the schema already describes language_code as an optional filter. The description adds no new information about the parameter beyond what the schema provides, so the baseline 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 clearly states the tool retrieves a comprehensive list of Text to Speech voices, with a specific verb ('Retrieves') and resource. It also distinguishes itself from sibling tools by explicitly noting it must be called before generating any audio, which is the key differentiator.

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 usage guidance: 'You must call this tool first to obtain the required voice object before you can generate any Text to Speech audio.' This clearly indicates when to use this tool versus generate_tts, and the optional filtering by language_code adds practical context.

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 transparency. The verb 'retrieves' clearly indicates a read-only operation, and it discloses the output data (requests used and remaining). It also scopes to the current user, adding useful 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 two sentences, well-structured, and front-loaded with the primary action. The first sentence states what it does, and the second gives a direct usage suggestion. No unnecessary words or repetition.

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 simple, zero-parameter read-only tool, the description is complete. It explains the resource, the data available, and a use case. It doesn't get into edge cases like errors or authentication, but these are not essential for this straightforward tool.

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, so the schema is empty. The description doesn't need to explain any parameters, and the baseline for 0-parameter tools is 4. No additional parameter information is necessary.

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 API rate limits, specifying the scope ('current user's') and the exact data returned (requests used and remaining). This clearly distinguishes it from sibling tools like generate_tts and get_voices, which are TTS-related.

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 explicitly says 'Use this to monitor your quota,' giving a direct use case. While it doesn't mention exclusions or alternatives, the tool's purpose is simple and unrelated to siblings, so this context suffices.

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