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 effectively adds context beyond basic functionality: it discloses that the tool 'calls MiniMax API and may incur costs,' which informs about external dependencies and financial implications. However, it doesn't cover other behavioral aspects like rate limits, error handling, or output format, preventing a perfect score.

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: the first sentence states the core purpose, and the second sentence adds crucial usage and cost notes. Every sentence earns its place with no wasted words, making it highly efficient for an AI agent.

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 (8 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, usage constraints, and cost implications, which are critical for this type of tool. However, it lacks details on output (e.g., what is returned, file format) and error handling, which would be needed for full completeness.

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 schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description, which applies here.

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: 'Generate a video based on text prompts.' It specifies the verb ('generate'), resource ('video'), and input type ('text prompts'). However, it doesn't explicitly distinguish this from sibling tools like 'image_to_video' or 'text_to_image', which would require a 5.

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 guidelines: 'Use only when explicitly requested by the user.' This clearly indicates when to use the tool (user request) and implies when not to use it (without explicit request). It also mentions cost implications ('may incur costs'), adding practical guidance.

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 adds valuable context beyond the input schema: it discloses that the tool 'calls MiniMax API' (external dependency), 'may incur costs' (financial implication), and references async mode and 'query_video_generation' for status checking (workflow behavior). However, it doesn't detail rate limits, error handling, or output format specifics.

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: the first sentence states the core purpose, and the note adds critical usage and cost context. Every sentence earns its place with no redundancy or waste. The two-sentence structure is efficient and clear.

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 (video generation with async options) and lack of annotations/output schema, the description is reasonably complete. It covers purpose, usage restrictions, cost implications, and hints at async workflow. However, it doesn't describe the output (e.g., file format, location details beyond schema) or error cases, leaving some gaps for a generative AI 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?

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'firstFrameImage' or 'prompt' further). Baseline 3 is appropriate when the schema does the heavy lifting, though the description could have highlighted key required 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?

The description clearly states the tool's purpose: 'Generate a video based on an image.' This specifies both the verb ('Generate') and resource ('video'), and it distinguishes from siblings like 'generate_video' (which likely uses different inputs) and 'text_to_image' (different output). However, it doesn't explicitly differentiate from 'query_video_generation' (a related async status checker).

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 guidelines: 'Use only when explicitly requested by the user.' This clearly defines when to use the tool (user request) and implies when not to use it (unsolicited). It also mentions cost implications ('may incur costs'), which further guides usage decisions. No alternatives are named, but the restriction is clear.

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 adds context about the api_host requirement, which is useful beyond the basic 'list' function. However, it doesn't describe other behavioral traits such as rate limits, authentication needs, or what the return format looks like (e.g., pagination, structure), leaving gaps 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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and a key constraint. There is no wasted language, and every sentence earns its place by providing essential information efficiently.

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 low complexity (1 optional parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the basic purpose and a constraint, but without annotations or an output schema, it doesn't fully explain behavioral aspects or return values, making it adequate but not comprehensive for agent use.

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, with the 'voiceType' parameter fully documented in the schema itself. The description doesn't add any meaning beyond what the schema provides, as it doesn't mention parameters at all. According to the rules, when schema coverage is high (>80%), the baseline score is 3, which applies here.

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 a specific verb ('List') and resource ('all available voices'), making it immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'voice_design' or 'voice_clone', which might also involve voice-related operations, so it doesn't reach the highest score.

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 some usage guidance by specifying that the tool is 'Only supported when api_host is https://api.minimax.chat,' which implies a prerequisite condition. However, it doesn't explicitly state when to use this tool versus alternatives like 'voice_clone' or 'voice_design', nor does it provide clear exclusions or comparisons with siblings, leaving usage context partially 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context about API costs and user request requirements, which are behavioral traits. However, it doesn't describe what the tool returns (file path? success status?), error conditions, or processing time expectations, leaving gaps for a mutation tool.

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 perfectly concise with three sentences that each earn their place: purpose statement, API cost warning, and usage restriction. No wasted words, and the most important guidance ('Use only when explicitly requested') is appropriately positioned.

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 6-parameter mutation tool with no annotations and no output schema, the description provides good usage guidance but lacks information about return values, error handling, or what constitutes successful completion. The cost warning and user request requirement help, but more behavioral context would be needed for full completeness.

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 6 parameters thoroughly. The description mentions 'prompt and lyrics' which aligns with the two required parameters, but adds no additional semantic context beyond what's in the schema descriptions. Baseline 3 is appropriate when 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: 'Create a music generation task using AI models. Generate music from prompt and lyrics.' This specifies the verb (create/generate), resource (music), and inputs (prompt and lyrics). However, it doesn't differentiate from sibling tools like 'text_to_audio' or 'voice_design' that might also generate audio content.

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 guidelines: 'Use only when explicitly requested by the user.' This clearly defines when to use the tool. The note about API costs ('may incur costs') also provides important context about when to be cautious with usage.

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 but offers minimal behavioral insight. It states format support and video exclusion but doesn't cover critical aspects like whether playback is blocking/non-blocking, audio output destination, error handling, or performance characteristics. The agent lacks context about how this tool behaves in practice.

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 brief with three short sentences that each add value: core function, format support, and video exclusion. It's front-loaded with the primary purpose and wastes no words, though it could be slightly more structured for readability.

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, no output schema, and a tool that performs an action (playback), the description is insufficient. It doesn't explain what 'play' means operationally (e.g., plays through system speakers, returns audio stream), success/failure conditions, or what happens after invocation. For an action-oriented tool, more behavioral context is needed.

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 adds no parameter-specific information beyond what's in the schema, maintaining the baseline score. No additional syntax, constraints, or usage examples are provided.

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 action ('Play') and resource ('an audio file'), making the tool's purpose immediately understandable. It distinguishes from video-related siblings by explicitly stating 'Does not support video,' though it doesn't differentiate from other audio tools like text_to_audio or voice_clone.

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 like text_to_audio or music_generation. It mentions format support (WAV and MP3) but doesn't explain use cases, prerequisites, or exclusions beyond the video limitation.

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 but only states it queries status without detailing behavioral traits like error handling, rate limits, or what the status response includes. It mentions the relationship to `generate_video` but doesn't explain if this is a read-only operation or has side effects.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and every part earns its place, 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 the tool's moderate complexity (querying task status) and no output schema, the description is minimally adequate but lacks details on return values or error conditions. With no annotations, it should provide more behavioral context to be fully complete for 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?

Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no additional meaning beyond what's in the schema, such as clarifying parameter interactions or usage nuances, resulting in a baseline score of 3.

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 verb ('query') and resource ('status of a video generation task'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential alternatives like checking task status through other means, which prevents a perfect score.

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 mentioning 'task_id returned by `generate_video` tool if `async_mode` is True,' suggesting when to use this tool. However, it lacks explicit guidance on when NOT to use it or alternatives for synchronous tasks, leaving some ambiguity.

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 successfully reveals several important behavioral traits: the tool calls an external API (MiniMax), may incur costs, has default behaviors for missing parameters (voice ID, directory), and saves files to specific locations. However, it doesn't mention error handling, rate limits, or authentication requirements, which would be helpful for a tool with 15 parameters and API dependencies.

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 perfectly structured with two focused paragraphs: the first explains the core functionality and default behaviors, the second provides critical usage warnings. Every sentence earns its place, with no redundancy or unnecessary elaboration. The information is front-loaded with the primary purpose immediately clear.

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 complex tool with 15 parameters, no annotations, and no output schema, the description does well by covering the core purpose, defaults, cost implications, and usage constraints. However, it doesn't describe the output format or what happens after file saving (e.g., returns file path, success confirmation), which would be important given the absence of an output schema. The cost warning and explicit usage guidance compensate somewhat for these gaps.

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 schema description coverage is 100%, so the schema already comprehensively documents all 15 parameters. The description adds minimal parameter semantics beyond the schema - it mentions that voice ID and directory have defaults when not provided, but doesn't explain the relationships between parameters or provide additional context about parameter interactions. This meets the baseline expectation when schema coverage is complete.

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 ('convert text to audio'), identifies the key resources involved ('with a given voice', 'save the output audio file'), and distinguishes it from siblings like 'play_audio' (which plays rather than creates) and 'voice_clone' (which clones rather than converts text). The verb+resource combination is precise and unambiguous.

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 only when explicitly requested by the user') and includes an important exclusion/warning about costs ('may incur costs'). It also distinguishes from alternatives by specifying this is for text-to-audio conversion, not other audio-related operations like playing or voice cloning available in 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 of behavioral disclosure. It adds valuable context beyond basic functionality: it discloses that the tool 'calls MiniMax API and may incur costs,' which is critical operational information not inferable from the schema. However, it doesn't mention rate limits, error handling, or output format details.

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 extremely concise and well-structured: a clear purpose statement followed by a critical note about API costs and usage restriction. Both sentences earn their place, with zero wasted words, and the most important information (cost warning) is front-loaded in the note.

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, API integration, cost implications) and no annotations or output schema, the description does well by covering the core purpose and critical behavioral context (API costs). However, it lacks details about the generated output (e.g., file format, resolution) and doesn't explain error cases or authentication requirements.

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 no parameter-specific information beyond the general 'text prompts' reference. However, with 100% schema description coverage, all 7 parameters are well-documented in the schema itself (e.g., 'prompt' for text input, 'aspectRatio' with enumerated values). The baseline score of 3 is appropriate since 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: 'Generate images based on text prompts.' This specifies the verb ('generate') and resource ('images') with the mechanism ('based on text prompts'). However, it doesn't explicitly differentiate from sibling tools like 'generate_video' or 'image_to_video' beyond the obvious difference in output type.

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 the tool: 'Use only when explicitly requested by the user.' This gives explicit guidance on user-driven invocation. However, it doesn't mention alternatives or when-not-to-use scenarios relative to siblings like 'generate_video' for video generation.

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 effectively adds context beyond the input schema by mentioning costs ('New voices will incur costs when first used'), API dependency ('calls MiniMax API'), and cost warnings. However, it lacks details on rate limits, error handling, or output format, which would enhance 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 appropriately sized and front-loaded, with the core purpose stated first, followed by important behavioral notes. Every sentence adds value: the first defines the tool, the second warns about costs, and the third provides usage guidelines. There is no wasted text, making it efficient 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 the complexity of a voice cloning tool with 5 parameters and no output schema, the description is reasonably complete. It covers purpose, costs, API dependency, and usage guidelines. However, it lacks details on output format (e.g., file type, location specifics) and error scenarios, which would improve completeness 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 description does not add any parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, the baseline score is 3. The description focuses on behavioral aspects like costs and usage guidelines rather than explaining parameters, which is acceptable given the comprehensive 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 purpose: 'Clone a voice using the provided audio file.' This specifies the verb ('Clone') and resource ('a voice'), making it distinct from sibling tools like 'list_voices' or 'voice_design.' However, it doesn't explicitly differentiate from 'text_to_audio' in terms of input source, which could be slightly improved.

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 guidelines: 'Use only when explicitly requested by the user.' This clearly indicates when to invoke the tool, and the cost warning ('may incur costs') helps the agent avoid unnecessary usage. It effectively guides the agent on when to use this tool versus alternatives.

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 adds valuable context: it discloses that the tool calls an external API (MiniMax) and may incur costs, which are critical behavioral traits not inferable from the schema alone. It doesn't detail rate limits or error handling, but this is sufficient for a high score given 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 appropriately sized and front-loaded: the first sentence states the core purpose, and the note adds essential context without redundancy. Every sentence earns its place, making it efficient and easy to parse.

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 (voice generation with external API calls) and lack of output schema, the description is somewhat complete but has gaps. It covers purpose, usage constraints, and cost implications, but doesn't explain return values or potential errors. With no annotations and no output schema, more detail would improve completeness, but it's adequate for a minimum viable score.

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%, meaning the schema already documents all parameters thoroughly. The description adds no specific parameter information beyond the general 'description prompts' hint. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description, which applies here.

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: 'Generate a voice based on description prompts.' This specifies the verb ('Generate') and resource ('voice'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'voice_clone' or 'text_to_audio,' which prevents a perfect score.

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 note provides clear usage guidance: 'Use only when explicitly requested by the user.' This establishes a specific context for when to invoke the tool. However, it doesn't mention when NOT to use it or name alternatives among sibling tools, such as 'voice_clone' for different voice generation methods.

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