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

With no annotations provided, the description must fully disclose behavioral traits. It only states the action ('Add instrumental backing') without clarifying whether it modifies existing files, requires authentication, has rate limits, or what happens to intermediate files. The text does not address destructive potential or 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 very concise (8 words) and front-loaded with the core action. It efficiently conveys the purpose without extraneous information. However, it could be slightly expanded to include key behavioral notes without losing conciseness, hence a 4 rather than 5.

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 output schema and 9 parameters, the description is inadequate. There is no mention of return values, error handling, or what happens after submission (e.g., task_id, polling). The description does not explain the 'wait' parameter's important distinction between sync and async modes, which is critical for usage.

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 56%, meaning the schema provides partial descriptions. The tool description does not add any explanation beyond what is already in the schema. For instance, 'tags', 'wait', and 'model' are described in the schema but the tool description offers no further semantics. Baseline score of 3 is appropriate as the description does not compensate for the missing parameter 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 uses a specific verb 'Add' and resource 'instrumental backing' along with the context 'to uploaded vocal audio via Suno'. It clearly distinguishes the tool from sibling tools like 'add_vocals' or 'cover_audio' by specifying the input (vocal audio) and output (instrumental backing).

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 lacks any guidance on when to use this tool versus alternatives. It does not mention prerequisites, constraints, or scenarios where other tools (e.g., 'generate_music' or 'cover_audio') would be more appropriate. No exclusions or context are provided.

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 must fully disclose behavioral traits. It does not mention authentication needs, rate limits, whether the operation is destructive, or the possibility of asynchronous execution (despite 'wait' and 'max_wait_seconds' parameters existing in the 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 very short (one sentence), which is concise, but it is also incomplete for a tool with 10 parameters and async behavior. It does not effectively use the limited space to convey critical 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?

For a complex tool with 10 parameters, no output schema, and async mode, the description is severely lacking. It does not describe return values, how to handle async results, or the purpose of key parameters like 'wait' and 'model'.

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 only 50%, but the description adds no parameter information beyond what the schema already provides. It does not explain the meaning of 'prompt', 'uploadUrl', 'model', or the async mode behavior, which would be valuable.

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 ('Add'), a clear resource ('AI vocals'), and context ('to uploaded instrumental audio via Suno'). It effectively distinguishes from sibling tools like generate_music (creates music from scratch) or add_instrumental (adds instrumental to vocals).

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 (e.g., generate_music, separate_vocals). No when-to-use or when-not-to-use information is included.

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 are absent, so the description carries full burden. It does not disclose processing time, supported formats, failure modes, or the max file size (10MB is only in schema). Minimal behavioral insight.

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?

Single 17-word sentence, front-loaded with key verb and resource. No wasted words.

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 input/output tool without output schema, the description is adequate but lacks context on quality, latency, or supported audio types. Could be more complete.

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 coverage is 100%, so baseline is 3. The description adds no meaning beyond the schema (e.g., no extra context for audio_url, filename, or download_dir).

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 ('isolate'), resource ('audio'), and output ('clean isolated audio'), with specific reference to ElevenLabs via kie.ai. It distinguishes from siblings like 'separate_vocals' by mentioning 'background noise,' but does not explicitly differentiate.

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?

No guidance on when to use this tool versus alternatives (e.g., separate_vocals). No when-not or prerequisites mentioned. The description only states the basic input/output flow.

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 must disclose behavioral traits. It only states the conversion action without mentioning whether it is read-only, has side effects, requires permissions, or handles errors, leaving key behavioral aspects unspecified.

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 concise sentence prefixed by 'NEW —', which front-loads relevance. It contains no fluff, but 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 output schema, the description fails to explain the output format or how the enhanced description is used, and does not integrate with sibling tools (e.g., generate_music) despite being a likely preprocessing step.

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 coverage is 100%, but the description adds value by providing an example input ('Pop, Mysterious') and clarifying the transformation purpose, which goes beyond the schema's minimal description 'Concise style description to enhance'.

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 converts concise style input into an enhanced style description for music generation, using a specific verb ('Convert') and resource ('style input'), and distinguishes it from generation siblings.

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 as a preprocessing step for music generation tools (e.g., generate_music) with the example 'Pop, Mysterious', but does not explicitly state when to use it versus alternatives or provide when-not-to-use 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?

No annotations are present, so the description carries the full burden of behavioral disclosure. It only states the action without revealing whether the operation is read-only, whether it consumes credits, or what the response format looks like.

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, short sentence that directly states the tool's purpose with no extraneous words. It is optimally concise and front-loaded.

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 parameterless tool, the description covers the essential purpose. However, it lacks any mention of return values or format, which would help the agent interpret the result. Given the simplicity, it is mostly complete.

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 zero parameters with 100% coverage, so no parameter documentation is needed. The description adds no parameter semantics, but this is adequate given the absence of 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 uses the specific verb 'check' and clearly identifies the resource as 'remaining kie.ai account credits'. Among sibling tools focused on generation and editing, this is the only credit-related tool, making its purpose 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?

No usage guidelines are provided. The description does not indicate when to use this tool versus alternatives, nor does it mention any prerequisites or context for checking credits.

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 must fully disclose behavior. It only states 'check status', omitting whether the tool is read-only, what the response contains, or any side effects. Minimal 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?

A single sentence that is front-loaded and to the point, with no wasted words. Efficiently conveys the core action.

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 tool with one parameter and no output schema, the description is minimally adequate but lacks details on response content, error handling, or behavior under different task states. Enough to understand basic function but not fully complete.

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 one string parameter (task_id) with 0% description coverage. The description mentions 'by taskId', adding context that task_id is the identifier, but does not explain format, origin, or examples. Some value added but insufficient for full compensation.

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 'check' and the resource 'status of a kie.ai generation task' with the mechanism 'by taskId'. It distinguishes itself from sibling tools like list_tasks, which lists tasks, and generate_*, which create tasks.

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 you have a taskId and want to query its status, but provides no explicit guidance on when to use this tool versus alternatives like list_tasks (which might also show status) or when not to use it.

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 must disclose behavioral traits. It mentions the download path but does not disclose safety, destructiveness, or other important behaviors like blocking vs. async mode, rate limits, or permissions.

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, consisting of just two sentences that get straight to the point. No unnecessary 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?

Despite having 6 parameters and no output schema, the description does not explain the tool's behavior in sync vs async mode, return values, or how it interacts with other tools like check_task and download_result. This is insufficient for a tool that requires polling in async mode.

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 83%, so the schema already explains parameters well. The description adds no extra semantic value beyond what is in the schema, so baseline 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 action ('Convert a Suno track to lossless WAV format') and provides the download location. However, it does not explicitly distinguish from other audio conversion tools among the siblings.

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 lacks any guidance on when to use this tool versus alternatives. No mention of prerequisites, context for conversion, or integration with other tools like check_task or download_result.

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, the description must disclose behavioral traits but only mentions 'via Suno'. It fails to state whether the operation is synchronous/asynchronous, requires specific permissions, or has side effects (e.g., saving files). The wait parameter's behavior is documented in the schema, not the description.

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?

Single sentence is concise and front-loaded, but could include more detail without becoming verbose. Every word has purpose, but the description is under-specified given the tool's complexity.

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 13 parameters, no output schema, and no annotations, the description is far from complete. It does not explain return values, error handling, or async behavior, which is critical for an AI agent to use the tool 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 54% (moderate). The description adds minimal semantic value by hinting at parameters like style, vocalGender, and instrumental. However, important parameters like wait, model, prompt, and download_dir are not addressed, leaving gaps that the schema partially covers.

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?

Description clearly states verb 'Create an AI cover' and resource 'from uploaded audio'. Distinguishes from sibling tools like generate_music (generates music from scratch) and generate_sfx (sound effects) by specifying it's a cover with custom vocals, style, and instrumentation via Suno.

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?

No explicit guidance on when to use this tool vs alternatives like generate_music or generate_tts. The description does not provide use cases or exclusions, leaving the agent to infer based solely on the tool name and brief purpose.

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?

Description discloses the download destination (kie/assets/raw/), but without annotations, it fails to detail other behavioral traits like wait mode, error handling, or rate limits. Some transparency but 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?

Two sentences, no filler, front-loaded with purpose. Highly concise.

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 8 parameters, 2 required, no output schema, and no annotations, the description is too brief. It does not explain the full workflow, return format, or dependencies on other tools like check_task.

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 high (88%), so description adds marginal value beyond the schema. It provides context for download_dir default but does not explain other parameters like author or domainName beyond their schema 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?

Description clearly states it generates an MP4 music video from a Suno track and specifies the download destination. However, it does not differentiate from sibling tools like generate_video, which could be ambiguous.

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?

No guidance on when to use this tool versus alternatives like generate_video, or prerequisites such as needing a Suno generation task. Usage context is 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?

No annotations are provided, so the description bears full burden. It mentions the combination of image and optional voice and the return of a characterId, but does not disclose side effects, error handling, limitations on image URLs, or permission requirements.

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 very concise, with three short, front-loaded sentences. No wasted words, but could be slightly more structured to separate purpose, behavior, and output.

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 has 4 parameters (2 required) and no output schema, the description is incomplete. It does not explain constraints on image URLs, how characterId is formatted, or how this tool fits into the video generation workflow.

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%, providing clear parameter descriptions. The tool description adds 'Combines image + optional voice' and 'Returns characterId', slightly extending schema info but not substantially.

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 ('Create'), the resource ('reusable visual character for Gemini Omni video generation'), and distinguishes from siblings like 'create_omni_voice' by specifying that it combines image with optional voice.

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 for creating characters but does not explicitly state when to use this tool versus alternatives (e.g., generate_video) or provide prerequisites. Lack of exclusions limits 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that the tool creates a reusable voice character and returns a kieAudioId, but fails to explain mutation implications, authorization needs, rate limits, or any side effects. The description is too brief to be fully transparent.

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: two sentences covering purpose, newness, use case, and output. Every sentence adds value with no wasted words. Front-loaded with 'NEW' and the core action.

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 moderate complexity of creating a voice character, the description covers the essential purpose and output linkage. However, it omits details on customization behavior and validation beyond what the schema provides. Still, it is largely complete for a well-schematized 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 each parameter. The description adds no new parameter-level meaning beyond the schema. Baseline 3 is appropriate as the description does not need to compensate for coverage gaps.

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 (create), resource (reusable voice character), and context (Gemini Omni video generation). It also specifies the return value linking to generate_video. However, it does not explicitly differentiate from sibling tools like create_voice_clone, which creates a different type of voice resource.

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 mentions a specific use case (video generation) and links to generate_video, but does not provide explicit guidance on when to use this tool versus alternatives such as create_voice_clone or prepare_voice_clone. No when-not or exclusion criteria are given.

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 exist, so the description carries the full burden. It highlights experimental status and unverified end-to-end flow but does not disclose what happens on failure, required permissions, or state changes beyond returning a voiceId.

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 at one sentence, front-loading key info (EXPERIMENTAL, STEP 2). Every word adds value without fluff.

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 8 parameters and no output schema, the description is somewhat sparse. It omits details like verification URL format, timeout behavior for max_wait_seconds, and failure handling, leaving gaps for the 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?

With 75% schema coverage, most parameters already have descriptions. The tool description adds no extra meaning beyond what's in the schema, so it meets the baseline but does not improve 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 it is the second step after prepare_voice_clone, requiring the verification recording. It specifies the outcome (voiceId) and usage context (generate_music), distinguishing it from siblings like prepare_voice_clone and regenerate_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 explicitly tells when to use (after prepare_voice_clone, after recording the verification phrase) but does not provide exclusions or alternatives for failure 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. It does not mention error handling, authentication, or whether files are overwritten, leaving significant behavioral 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?

A single, front-loaded sentence is concise with no wasted words. However, it may be too terse for important nuances.

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 3 parameters, no output schema, and zero annotations, the description is insufficient. It lacks details on return values, error cases, and workflow context among siblings.

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 coverage is only 33% (only download_dir has a description). The description adds default directory info but does not explain task_id or filename, which are crucial but undocumented.

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 'Download a completed task result' and the default target directory 'kie/assets/raw/', making the purpose apparent. However, it implies a fixed destination while the schema allows a custom directory, causing slight inconsistency.

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?

No guidance on when to use this tool versus alternatives (e.g., upload_file) or prerequisites like task completion. The agent is left to infer usage 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?

No annotations are provided, and the description only gives a brief behavioral hint (requires audioId). It does not disclose important traits like processing time, error handling, or whether the original track is modified. The schema parameters like wait and max_wait_seconds imply async behavior but are not explained in the description.

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 concise sentences, front-loading the core purpose and essential prerequisite. Every word adds value with no redundancy.

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 11 parameters and no output schema, the description is too sparse. It does not explain the return value (task ID?), the async workflow, or how to proceed after submission. The agent would need to infer behavior from sibling tool names like check_task.

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 73%, and the description adds value by linking audioId to generate_music. However, it does not enhance understanding of other parameters like continueAt or style beyond what the schema already provides. Baseline is met but not exceeded.

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 'extend/continue' and the resource 'existing Suno track', and specifies the prerequisite 'audioId from a previous generate_music task'. It effectively distinguishes from siblings like generate_music which creates new tracks.

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 mentions the prerequisite of needing an audioId from generate_music, but does not provide guidance on when to use this tool versus alternatives like cover_audio or add_instrumental. No explicit 'when-not-to-use' or alternative naming is given.

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 only the 'one call per taskId' constraint, but fails to mention if the operation is destructive, what happens on repeated calls, authentication needs, or rate limits. The lack of behavioral context makes it risky for an AI 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 two sentences, fully front-loaded with purpose in the first sentence and a key constraint in the second. No wasted words; every sentence adds value.

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 tool with 3 parameters and no output schema, the description provides core purpose and a constraint, but omits expected output type, file format, or error handling. Given the absence of output schema, more detail on return values would help 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 coverage is 100%, but the description adds important context: 'Auto-generated if omitted' for filename and a crucial warning about absolute directory paths for download_dir. 'One call per taskId' also relates to parameter usage, enhancing understanding beyond the 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 verb 'generate' and the specific resource 'album cover art image for an existing Suno music track'. It distinguishes from sibling tools like 'generate_image' (general image generation) and 'create_music_video' (video). The constraint 'One call per taskId only' further clarifies the scope.

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 (existing music track) and a constraint (one call per taskId), but does not explicitly state when to use this tool over alternatives like 'generate_image' or what prerequisites are needed. There is no guidance on when not to use it.

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 full burden. It mentions the download destination and the service used, but lacks details on side effects, cost, rate limits, or error behavior. The schema provides async mode details, but the description itself is minimal.

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 concise sentences, front-loaded with the action verb and resource. Every word adds value with no redundancy.

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 tool has 7 parameters and uses a complex async workflow, but the description is minimal. The schema compensates with thorough parameter documentation, yet the description does not cover return values or error handling. Adequate 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?

Schema description coverage is 100%, so the schema already documents all parameters well. The description adds no extra meaning beyond noting the download directory, so a 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 generates multi-speaker dialogue, mentions the underlying service (ElevenLabs Text-to-Dialogue V3 via kie.ai), and indicates the output location. This distinguishes it from sibling tools like generate_tts (single speaker) and generate_music (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 says 'Great for conversations between characters,' implying a use case, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or constraints.

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 exist, so the description carries full burden. It thoroughly discloses the tool's behavior: it uses Google Gemini, supports style direction and tone tags, has credit cost (~4.2 credits/min), model trade-offs (flash quality degrades on long takes, pro stable), async mode via wait=false, and default download location. This is comprehensive behavioral 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 approximately 150 words, tightly packed with key information: novelty ('NEW'), backend, capabilities, pricing, modes, model comparison, and output location. It is front-loaded with the most critical identifier ('Google Gemini native TTS'). No redundant sentences; every sentence earns its place.

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 12 parameters and no output schema, the description covers core functionality, limitations, async workflow, and pricing. It explains the two modes and model selection well. However, it does not describe the return format (e.g., whether it returns a file path or URL after download) or error cases, leaving a minor gap in 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 92% (11/12 params have inline descriptions). The description adds high-level context about modes and credits but does not significantly elaborate on individual parameter meanings beyond what the schema already provides. For example, 'scene' and 'sample_context' are better explained in the schema than in the description. Baseline 3 is appropriate as the description adds moderate value but does not deeply augment 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 clearly states it is a Google Gemini native TTS tool with style-directed speech, 30 named voices, up to 2 speakers, and inline tone tags. It distinguishes itself from siblings (generate_tts, generate_dialogue) by highlighting the Gemini backend, pricing, and dual modes (simple/dialogue). The verb 'generate' and resource 'gemini_tts' are specific 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 each mode (Simple vs Dialogue) and which model to choose (flash for expressive <60s, pro for stable long-form). It also mentions pricing and compares to ElevenLabs. However, it does not explicitly name alternative tools (e.g., generate_tts) for cases where Gemini is not desired, slightly limiting differentiation from siblings.

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, the description carries the behavioral transparency burden. It discloses the download side-effect, model-specific credit costs (e.g., '3cr cheapest', '24cr'), and speed hints ('~4s'). While it does not cover auth, rate limits, or error handling, it provides meaningful resource-usage context that helps an agent anticipate the tool's impact.

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 and output location, and the MODEL GUIDE condenses a large amount of model-selection knowledge into a dense, scannable block. It is lengthy but each mapping serves a purpose; minor improvement would be converting the guide into bullet points for even faster scanning.

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 tool is complex (9 parameters, no output schema), but the description covers model selection, output location, and costs. The schema's parameter descriptions complement it by detailing async behavior (wait) and download_dir overrides, making the overall definition reasonably complete for an agent to invoke 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 coverage is 100%, so the baseline is 3. The description goes well beyond the schema by providing a comprehensive MODEL GUIDE that recommends specific models for specific use cases, cost tiers, and model options. This adds substantial semantic meaning to the 'model' parameter and related options.

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 explicitly states 'Generate an image using kie.ai (47+ models). Downloads to kie/assets/raw/.' This clearly identifies the specific verb (generate), resource (image), and output behavior, distinguishing it from sibling tools like generate_video or generate_music.

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 MODEL GUIDE provides explicit when-to-use guidance, mapping use cases (architecture, game art, anime, etc.) to specific models with cost and speed notes. It also points to list_models for exploration and explains a generate-then-refine workflow using sibling tools (grok_segment_map, grok_image_edit), going far beyond simple 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?

No annotations provided, so description must disclose all behavioral traits. Mentions output is text, no file download, and max 200 characters. Adequate but lacks details like rate limits, API key requirements, or whether generation is synchronous.

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 sentences, no fluff. Every word is meaningful.

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 single-parameter tool without output schema or annotations, the description sufficiently covers purpose, input constraints, and output type. Could mention that prompt should be lyrics-style or something, but not necessary.

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 coverage is 100% with description for prompt. Tool description reinforces the max length constraint but adds no new semantic information beyond what schema provides. Baseline score 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?

States it generates song lyrics from a prompt using Suno AI, which is specific. Does not explicitly differentiate from siblings like generate_music, but the resource 'lyrics' implies a distinct purpose.

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?

No when-to-use or when-not-to-use guidance. Does not mention alternatives among the many 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?

No annotations are provided, so the description carries full burden. It only states the basic operation without disclosing behavioral traits such as whether it is destructive (e.g., overwriting), requires authentication, or has rate limits. The 'NEW' tag indicates novelty but adds no behavioral insight.

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, front-loaded sentence with no wasted words. It is concise and immediately conveys the tool's core 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 complexity (8 parameters, 1 required) and no output schema, the description is too sparse. It does not explain return values, async behavior (though wait parameter is described in schema), or how parameters interact. The description fails to provide a complete picture for an agent to use the tool effectively.

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 75%, so baseline is 3. The description adds minimal value beyond the schema, mentioning only 'audioIds from previous generations' which is already in the schema. No additional context for other parameters like wait, model, or prompt.

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 'Mashup', the resource 'Suno tracks', and the output 'into one new track'. It distinguishes the tool from siblings like generate_music or cover_audio by specifying it combines existing tracks.

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 a prerequisite ('Provide audioIds from previous generations') but offers no guidance on when to use this tool versus alternatives like generate_music or cover_audio. It does not specify when not to use it or mention alternative 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?

Discloses async behavior and download directory, but no annotations are provided. Missing details on file conflicts, naming, or authentication requirements, which would be needed for full 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?

Two sentences, front-loaded with the main purpose. Efficient but could be slightly more structured with bullet points for parameters.

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?

Covers core functionality and async mode but does not describe return values or error scenarios. Adequate for a simple export tool given no output 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 coverage is 83%, and the description adds context for the wait parameter and directory, but most parameter meaning is already clear from the schema. Adds marginal value.

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 'Export a Suno track to MIDI notation' and specifies the output location, distinguishing it from sibling tools like generate_music or download_result.

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?

No explicit guidance on when to use this tool versus alternatives, such as other export tools or polling mechanisms. The async mode hint is present but not contextualized.

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 must carry behavioral disclosure. It discloses polling behavior and download path ('Polls until done and downloads to kie/assets/raw/'), which adds value. However, it does not mention authorization needs, rate limits, or potential costs (e.g., credit usage), leaving gaps for a generation 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 three concise sentences with clear front-loading. The first sentence states the core purpose, the second lists supported models and duration, and the third gives use cases and behavior. No superfluous words.

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?

No output schema exists, so the description should cover return values. It mentions polling and download path but does not describe what the tool returns (e.g., a result object, task_id for async, or downloaded file path). Given the tool's complexity (9 params, generation), this omission is notable.

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 baseline is 3. The description does not add significant meaning beyond the schema; it only restates model versions and duration. The schema already provides detailed parameter descriptions, so no additional value is needed.

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 generates music using Suno via kie.ai, lists supported models (V5.5, V5, V4.5+, V4.5, V4) and duration (up to 8 minutes), and specifies use cases (game music stems, ambient tracks, jingles). This verb+resource combination effectively distinguishes it from sibling tools like generate_sfx or generate_video.

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 for game music, ambient, and jingles but provides no explicit guidance on when to use this tool vs alternatives (e.g., generate_sfx for sound effects, generate_gemini_tts for speech). No when-not-to-use or alternative mentions are present.

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, description must disclose behavioral traits. It mentions 'reusable music character' and the prerequisite, but does not state side effects, auth needs, or what happens to the existing track. Minimal insight beyond basic action.

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?

Extremely concise – two sentences. Front-loaded with 'NEW' to indicate freshness. No fluff; every word earns its place.

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?

Description is functional but omits broader context: what a Suno Persona is, how to use it post-creation, or what the API returns. Given 7 parameters and no output schema, more context would aid 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 coverage is 100% (all parameters described). Description adds no extra meaning beyond the schema, e.g., the prerequisite is already in taskId description. Baseline 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?

Explicitly states it creates a Suno Persona from an existing Suno track, with specific verb and resource. Clearly distinguishes from sibling generation tools like generate_music or cover_audio by focusing on persona creation.

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?

Provides a clear prerequisite (taskId from V3.6+ generation) but lacks guidance on when to use persona creation versus alternatives like cover_audio or generate_music. No explicit when-not or comparison to siblings.

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?

Discloses the underlying model (Suno V5) and removal of ElevenLabs model, mentions output directory (kie/assets/raw/), and via parameter descriptions explains async mode. However, with no annotations, the description does not fully cover safety, permissions, or guarantees. Provides some behavioral context but not 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?

Extremely concise: three sentences. Front-loaded with core action, followed by examples and a sibling distinction. No wasted words.

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?

Describes model, use case, and alternative, but omits the return value/format (no output schema). While parameter descriptions explain async polling, the description lacks an overall picture of what the agent receives upon completion.

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 coverage is 100% with detailed parameter descriptions. The description adds value by suggesting use cases (game sounds) and directing to alternatives, but does not significantly augment the schema's per-parameter meaning.

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?

Clearly states it generates a sound effect from text via Suno V5, with specific examples (game sounds: UI clicks, magic spells, etc.). Explicitly distinguishes from sibling generate_sounds for loop/BPM/key control.

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?

Provides a clear when-to-use scenario (game sounds) and an explicit alternative (generate_sounds for loop/BPM/key control). However, lacks guidance on when not to use compared to other sound generation siblings (e.g., generate_tts, generate_dialogue).

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, the description carries full burden but only mentions the download path and 'via Suno'. It omits important traits like async task polling, side effects, and rate limits, leaving 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?

Two efficient sentences with no wasted words. Front-loaded with core purpose and key attributes, making it easy 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 tool with 10 parameters and no output schema, this description is too sparse. It lacks return value info, workflow explanation, and integration of key concepts like async generation, which are critical for correct usage.

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 coverage is 70%, and the description adds context for BPM, key, and loop control parameters. However, it does not elaborate on other crucial parameters like wait or filename, so value above schema is moderate.

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 it generates loopable sound effects with BPM, key, and loop control via Suno, specifying the verb and resource. However, it does not explicitly differentiate from similar sibling tools like generate_music or generate_sfx.

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?

No guidance is provided on when to use this tool versus alternatives. Given many sibling audio tools, explicit usage context is missing.

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. It discloses model variants, async behavior, download location constraints, and voice_id limitations (curated set, rejection of arbitrary IDs). However, it does not explicitly state side effects (file creation) or auth requirements.

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 sentences that pack essential information with no redundancy. First sentence establishes core purpose and key differentiators; front-loaded for quick comprehension.

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 9 parameters and no output schema, the description covers models, async mode, and file location well. Missing details on return value (e.g., file path vs task ID) and output file format (e.g., .mp3). Adequate but not exhaustive.

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?

With 100% schema coverage, the description adds significant value beyond the schema: explains wait rationale, voice_id caveats, download_dir absolute path requirement, and model selection advice. Each parameter gets meaningful context.

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 'Generate speech from text', identifies the technology stack (ElevenLabs via kie.ai), and specifies output destination. This distinguishes it well from sibling tools like generate_music and generate_sfx.

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 context (model selection, async mode via wait parameter) but lacks explicit guidance on when to prefer this tool over alternatives like generate_gemini_tts. No comparative analysis with 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?

No annotations are provided, so the description carries the full burden. It discloses download behavior, model-specific costs, and upstream pauses. However, it does not mention potential failure modes, rate limits, or authentication requirements, which would be useful for a generation 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 dense and information-rich, but it is quite long and could be overwhelming. It front-loads the core purpose and then provides a model guide, which is useful but could be better structured with bullet points or sections. Every sentence adds value, but the length may reduce 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 the tool's complexity (9 parameters, nested objects, no output schema), the description covers model selection, async mode, and output location well. It lacks details on model_options structure and error handling, but the pointer to list_models for exploration helps fill 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?

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining model selection trade-offs and the async wait behavior, which complements the schema. It also clarifies the download_dir default and the working directory caveat, which is not obvious from the schema alone.

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 generates videos using kie.ai with 86+ models and specifies the output location. It distinguishes itself from siblings by covering video generation specifically, while other tools handle music, audio, or extensions.

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 extensive guidance on when to use which model, including cost, speed, and use-case recommendations. It also explicitly mentions alternatives like veo_extend and runway_extend for video extension, and notes that Sora 2 is paused, preventing misuse.

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 disclosing behavioral traits. It only states what the tool returns (timestamped lyrics) but does not mention side effects, idempotency, network calls, or any caveats. For a tool that likely performs an API fetch, the description lacks necessary behavioral 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 extremely concise: one short sentence plus a 'NEW' prefix. It is front-loaded with the core functionality and uses, wasting no words. Every part earns its place.

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 output schema, the description should compensate by explaining the return format (e.g., structure of timestamped words). It mentions use cases but omits what the agent will actually receive. For a simple 2-param tool, the missing output details reduce 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% (both parameters have meaningful descriptions like 'Suno task ID' and 'Audio ID from sunoData'). The description adds no additional parameter information beyond the schema, so it meets the baseline 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 tool's function: 'Get word-level timestamped lyrics from a Suno track.' It uses a specific verb ('Get'), specifies the resource ('word-level timestamped lyrics'), and names the source ('Suno track'). This distinguishes it from siblings like 'generate_lyrics' (which creates lyrics) and 'speech_to_text' (which transcribes audio), though it does not explicitly contrast them.

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 context for when to use the tool: 'Useful for karaoke, captioning, or sync.' However, it does not offer guidance on when not to use it or mention alternative tools. Given the large set of sibling tools, explicit exclusions or comparisons would improve this dimension.

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?

Without annotations, the description carries the burden of transparency. It explains that regions outside the masks are preserved, that a new full image is returned, the cost per round (4 credits), and the download location. This gives a clear picture of the tool's behavior and side effects, though it does not explicitly mention potential failure modes.

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 and covers key points without excessive verbosity. Some phrases are repeated (e.g., mention of task_id and mask_indexs appear twice), but it remains concise 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, the description addresses the essential aspects: purpose, prerequisites, usage steps, output details, and cost. It does not cover error handling or edge cases, but for an editing tool with clear inputs, it is adequately complete.

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 descriptions already cover each parameter clearly (task_id, prompt, mask_indexs, filename, download_dir). The tool description adds valuable context, such as the need to run grok_segment_map first, the meaning of mask_indexs, and defaults. This fully explains the parameters and their roles.

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: editing only selected regions of an image generation, specifying the required inputs (task_id, prompt, mask_indexs) and the intended use case. It also distinguishes itself by referencing the prerequisite grok_segment_map tool.

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?

Provides explicit instructions: run grok_segment_map first, pass the task_id and mask indices, and explains the iterative refinement capability. However, it does not explicitly state when to avoid using this tool or compare it to alternatives, but the guidance is sufficient for the intended 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?

No annotations are present, but the description discloses key behaviors: it returns region index, semantic name, and mask PNG URL, and notes it is free. This adds useful context beyond the bare 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 slightly verbose but well-structured, covering purpose, usage, output, and cost in a logical flow. It is not excessively redundant and remains readable.

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 only one parameter and a clear schema, the description sufficiently explains the tool's role in the workflow, its input constraints, and its output format, making it complete for an agent to decide when and how to use it.

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 already describes the task_id parameter in full (Task ID from a completed generate_image call with model grok-imagine-image-2-0/text-to-image). The description mainly repeats this requirement without adding new parameter-specific 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?

Explicitly states the tool segments a Grok Imagine Image 2.0 generation into named regions for targeted editing, with a clear verb and resource. It is distinct from sibling tools like generate_image and grok_image_edit.

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?

Provides clear usage conditions: only works on task_ids from a Grok Image 2.0 generation, and outlines a recommended workflow (generate_image -> grok_segment_map -> grok_image_edit).

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 provided, so description carries full burden. It clearly states the action (list) and output content (models with options). No mention of pagination or rate limits, but for a read-only list tool, this is sufficient and not misleading.

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?

Single sentence that conveys purpose. It is concise but could be slightly more structured (e.g., mentioning that it returns a list). No wasted words, but could include more detail.

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?

No output schema exists, so description should clarify return structure. It mentions 'aspect ratios and model-specific options', which gives a hint but not full details. For a simple list tool, it is adequate but not thorough.

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% (both parameters are described). The description does not add new meaning beyond the schema; it echoes 'aspect ratios and options' which aligns with parameter 'verbose'. Baseline 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 lists all available kie.ai models and includes aspect ratios and options. It distinguishes from sibling tools like list_tasks and check_credits by focusing on models.

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?

No explicit guidance on when to use this tool versus alternatives. Usage is implied (e.g., before generation), but no conditions or exclusions are provided. Sibling tools are diverse, so guidance would be helpful.

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, the description carries full burden. It implies a read-only, non-destructive operation. However, it does not disclose details like permissions required, behavior when the directory is empty, or if any filtering is applied.

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?

Single sentence, no redundancy, front-loads key information. Every word serves a purpose.

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 output schema, the description should explain return format. It mentions 'list all files' but not the output structure (e.g., strings, objects). For a simple tool with no params, it is adequate but incomplete for a full 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?

There are zero parameters, so the description's job is minimal. Baseline is 4. The description adds no extra meaning beyond the schema, but no value is needed.

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 ('List all files'), the target resource ('in kie/assets/raw/'), and the context ('waiting to be processed'). It distinguishes from sibling 'list_tasks' by specifying a directory and state.

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 lacks explicit guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusion criteria, or comparison with sibling tools like 'list_tasks'.

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 must disclose behavioral traits. It mentions listing recent tasks from the session, but does not clarify what 'recent' means, the nature of the session, or whether the operation has any side effects (though likely read-only). Minimal 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 a single, concise sentence with no extraneous information. It is front-loaded with the verb and resource, making it easy to scan.

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 list tool with one parameter and no output schema, the description is minimally adequate but missing parameter explanation and clarification of 'recent' and 'session'. It does not reference return format or pagination. Given the simplicity, a score of 3 is appropriate.

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 mention the 'limit' parameter at all. With 0% schema description coverage, the agent has no additional context about what the limit does or how it affects results beyond the schema default of 10.

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 'List' and the resource 'recent image generation tasks from this session'. It distinguishes from siblings like 'check_task' (which likely checks a single task) and various generation tools by specifying the scope of recent session tasks.

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?

No explicit guidance on when to use this tool versus alternatives like 'check_task'. The description implies it is for listing, but does not state when not to use it or what to do if you need a different perspective.

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, the description fully carries behavioral disclosure: it notes the tool is experimental (#20), the two-phase process (submit + callback), and caveats about the completion step not being verified end-to-end and requiring a real callback URL. This is thorough for a tool with no 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 three sentences with no wasted words. It front-loads the tool's identity (experimental, step 1, free) and efficiently covers the process, warnings, and limitations.

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, no output schema), the description explains the workflow and key risks adequately. It could detail error scenarios or return values, but for a step-1 tool, the provided information is sufficient for an agent to use it 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 coverage is 83%, so baseline is 3. The description adds context beyond schema by explaining the polling behavior for 'wait' and 'max_wait_seconds', and the callback server expectation. It does not detail each parameter but provides workflow context that aids usage.

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 it is 'STEP 1 of Suno custom-voice cloning' and describes the action: submit a clean vocal sample, poll to wait_validating, and trigger a callback. It distinguishes from its sibling 'create_voice_clone' which is the next step.

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 marks the tool as step 1 and directs to use 'create_voice_clone' afterward. It includes a warning about only cloning owned voices. However, it lacks explicit 'when not to use' or alternative tools for other 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?

With no annotations, the description bears full responsibility but only notes it's experimental (version #20) and does not disclose side effects, such as whether retrying overwrites previous results or the nature of the response.

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 sentence, front-loading the experimental nature and core action with no wasted words.

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 tool with one parameter and no output schema, the description covers the purpose and parameter but omits details about return value, idempotency, or what happens on retry.

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 coverage is 100%, so baseline is 3. The description adds the qualifier 'failed/incomplete' to the parameter, providing marginal extra meaning beyond the 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 verb 'retry' and the resource 'custom-voice task', specifies it is for failed/incomplete tasks, and distinguishes it from sibling tools like create_voice_clone and prepare_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 implies usage when a custom-voice task failed or is incomplete, but does not explicitly state when not to use it or mention alternative tools for similar purposes.

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 for behavioral disclosure. It only states the basic operation ('replace a time range') without revealing side effects (e.g., whether the original track is modified, whether replacement is permanent, or any authentication or rate-limit considerations).

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, well-formed sentence that front-loads the core purpose. However, it could be slightly restructured to mention key parameters or usage notes without adding verbosity, so it earns a 4 rather than a 5.

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 the tool (13 parameters, 5 required, no output schema, no annotations), the one-sentence description is insufficient. It does not explain return values, error states, or how the replacement process works (e.g., whether the rest of the track is preserved).

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 adds no additional meaning beyond the input schema. While schema description coverage is 69%, meaning some parameters are partly documented in the schema, the tool description itself does not explain the purpose or relationships of the parameters (e.g., how 'infillStartS' and 'infillEndS' define the target range).

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 ('Replace'), names the resource ('a time range in a Suno track'), and specifies the action ('with new AI-generated content'). It clearly distinguishes this tool from siblings like 'extend_music' or 'cover_audio' which modify the full track or add sections rather than replace a segment.

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, nor any exclusions or prerequisites. For example, it does not mention that the original track must exist or that replacement may require specific credits.

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 must fully disclose behavioral traits. It only states the purpose without mentioning whether the operation is destructive, requires authorization, has rate limits, or any side effects. Minimal transparency 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 a single, well-formed sentence with no extraneous words. It is front-loaded with the action and resource. Could be slightly more informative but remains concise 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 has 5 parameters, no output schema, and sibling tools for other platforms, the description is incomplete. It lacks details on return values, error handling, or differentiation from similar extension tools like veo_extend. More information is needed for an agent to use it correctly without confusion.

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 describes most parameters (prompt, task_id, quality, download_dir) with coverage estimated at 60%. The description adds no additional meaning beyond what the schema provides. For filename, the schema lacks description, but the description does not compensate. Baseline of 3 is appropriate given moderate schema coverage.

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 extends an existing Runway Aleph video with continuation content, specifying the verb (extend) and resource (existing video). It distinguishes from sibling tools like generate_video (creation) and veo_extend (Veo platform).

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?

No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage only for extending existing Runway Aleph videos but does not mention alternatives or exclusions. The context of sibling tools helps, but the description itself lacks explicit guidelines.

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 provided, so description carries full burden. It discloses the download location ('kie/assets/raw/'), but does not mention destructiveness, authentication, rate limits, or other behavioral traits. Partial 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?

Two sentences, concise and front-loaded with core action. Every word earns its place without redundancy.

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 tool with 7 parameters and no output schema, the description is too minimal. It doesn't explain async mode, return values, or how to use results. Incomplete for effective 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?

Schema description coverage is high (86%), so baseline is 3. The description adds no parameter-specific details beyond the schema, but the schema already documents parameters well.

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 clear verbs 'Separate' and 'split' with specific resources (vocals, instrumentals, stems). It clearly states the tool's purpose and distinguishes from siblings like audio_isolation or add_vocals.

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?

No guidance on when to use this tool versus alternatives (e.g., audio_isolation). No mention of prerequisites or context. The description lacks explicit when-to-use or when-not-to-use information.

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, the description carries full burden for behavioral disclosure. It mentions supported features (diarization, audio event tagging) but omits important details: required authentication, limits on audio length or file size, latency, and whether the operation is destructive or read-only. The description provides moderate transparency but lacks depth.

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 long, entirely front-loaded with the core purpose. Every sentence adds value: first states the action and model, second lists supported features and output. No wasted words.

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 output schema and no annotations, the description covers the main purpose and supported features. However, it lacks details on output format (e.g., structure of transcription text, timestamps), error handling, and constraints (e.g., audio length, supported languages). It is fairly complete for a straightforward transcription tool but could be richer.

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 coverage is 100%, so baseline is 3. The description mentions diarization and audio event tagging, which correspond to boolean parameters, adding slight context. However, it does not elaborate on parameter formats, defaults, or interactions beyond what the schema already provides.

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 'Transcribe' and the resource 'audio to text', specifying the model (ElevenLabs Scribe v1) and key features (diarization, audio event tagging). It effectively distinguishes this tool from sibling tools like audio_isolation or generate_tts.

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?

No guidance is provided on when to use this tool versus alternatives such as audio_isolation or separate_vocals. The description does not mention any prerequisites, constraints, or use cases, leaving the AI agent without decision-making 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?

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions that the tool generates AI content and hints at async vs sync behavior via the wait parameter, but does not explain potential side effects, authentication needs, or what 'extend' entails precisely. Basic transparency is present but could be richer.

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 long with no wasted words. It front-loads the core purpose and the key exclusion (Suno tracks), making it highly efficient and scannable.

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 has 11 parameters and no output schema, the description provides the essential purpose and a key sibling distinction but lacks guidance on how to use parameters like continueAt, style, or prompt effectively. It is minimally complete for a complex 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 coverage is 64%, which is moderate. The description itself does not elaborate on parameters beyond what the schema provides. However, the schema descriptions for parameters are already quite informative (e.g., wait with detailed async explanation). The description adds minimal extra meaning, so it meets the baseline but does not exceed it.

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 extends uploaded audio files (not Suno tracks) with new AI-generated content, using a specific verb and resource. It explicitly distinguishes the tool from extend_music, making its purpose 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 explicitly tells when not to use the tool (for Suno tracks) and directs to extend_music as the alternative. This provides clear usage guidance and context for selection among siblings.

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 present, so the description carries the full burden. It discloses the 3-day expiration of files, the reliability of file_path over base64_data, the truncation issue with base64_data, and the requirement for publicly reachable URLs. It does not mention potential failure modes or permissions, but for a simple upload tool it covers key behavioral traits well.

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 (two sentences) and front-loaded with purpose. It packs critical usage guidance and behavioral notes without redundancy. Every sentence adds value, and the structure effectively guides the 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?

For a tool with 5 parameters and no output schema, the description covers all necessary aspects: what it does, when to use it, how to use each parameter, and key limitations. The mention of returning a public URL sufficiently covers the output, so no additional return details are 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?

With 100% schema coverage, the baseline is 3, but the description goes beyond schema by explaining the trade-offs between file_path, base64_data, and file_url, including when to prefer each and the truncation threshold. It also clarifies that file_path is read by the server itself, adding practical value.

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?

Description clearly states the action (upload file to kie.ai), the result (public URL), and the context (before generation tools). It distinguishes from sibling generation tools by framing it as a preparation step, and mentions specific use cases like image-to-image and video 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?

Explicitly provides usage guidance: 'Use this to upload local images/audio/video before passing them to generation tools' and 'PREFER file_path for local files'. It also clarifies when to use file_url vs file_path vs base64_data, including the warning about base64_data truncation, making it clear when to use 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?

No annotations provided, and the description does not disclose behavioral traits such as whether the original video is modified, permissions needed, output format, or error handling. The description carries full burden and fails to inform.

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 sentences with clear front-loaded purpose. No extraneous content. However, key information is missing, reducing the value of conciseness.

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 output schema, no annotations, and moderate complexity (6 params, many siblings), the description is incomplete. It lacks details on expected output, duration, effect on original, and error scenarios.

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 67% (4 of 6 parameters described in schema). The description adds no additional parameter context beyond the prerequisite. It does not explain the meaning of 'prompt', 'model', 'seeds', 'filename', or 'download_dir' in relation to the extension process.

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 extends an existing Veo 3.1 video with additional content, specifying the prerequisite (taskId). It distinguishes from generation tools but does not explicitly differentiate from other extension tools like runway_extend.

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?

Only mentions the prerequisite of having a taskId from a previous Veo generation. No guidance on when to use this tool over alternatives (e.g., generate_video, runway_extend) or when not to use it.

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. The description only states the action and prerequisite without disclosing behavioral traits like whether it is destructive, rate limits, or side effects. Carries minimal burden for 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?

Single sentence that is short and to the point, containing no wasted words.

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 explains the purpose and prerequisite, but lacks details on output/return values and behavioral impact. Suitable for a simple tool but still incomplete given no annotations or output 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 coverage is 75%. The description adds the context that the task must be completed, which is not in the schema. For other parameters, it adds no additional meaning beyond the schema 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 action (upscale), the target (Veo 3.1 video), the resolution (1080p), and a prerequisite (taskId from completed generation). It distinguishes from sibling tools like veo_upscale_4k.

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 explicitly requires a task ID from a completed Veo generation, providing clear context for when to use. However, it does not explicitly contrast with veo_upscale_4k or mention when not to use.

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 burden of behavioral disclosure. It mentions the operation takes 5-10 minutes and requires a prerequisite taskId, providing some transparency about latency and dependency. However, it does not disclose potential side effects, authorization needs, or result format, leaving 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 extremely concise, consisting of two sentences that deliver the core purpose, time expectation, and prerequisite. Every word serves a purpose with no redundancy or 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?

Given the tool has four parameters and no output schema, the description is incomplete. It does not explain how to handle the output (e.g., where the upscaled video is saved) or the role of parameters like 'index' and 'download_dir'. While the purpose and prerequisite are clear, additional context about workflow integration is missing.

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 coverage is 75% (three of four parameters have descriptions), but the tool description adds no new information about how to use the parameters. For example, 'filename' lacks a schema description and is not explained in the description. The description does not compensate for missing parameter details, so value beyond schema is minimal.

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 this tool upsamples a Veo 3.1 video to 4K resolution, distinguishing it from the sibling 'veo_upscale_1080p' which targets a different resolution. The verb 'Upscale' and resource 'Veo 3.1 video to 4K' are specific and actionable.

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 a prerequisite ('Requires taskId from completed Veo generation') and a time estimate, implying it should be used after a Veo generation. However, it does not explicitly contrast with alternatives like 'veo_upscale_1080p' or state when not to use this tool. The usage context is clear but lacks exclusion guidance.

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