NiceVois

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

Beyond the sparse annotations, the description reveals key behavior: the tool is free, requires no account, returns a one-time upload URL, and requires the agent to PUT the bytes and then call get_free_audio_job with the returned id. This gives the agent a concrete execution plan and sets expectations about the two-step process.

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 focused sentences front-load the user-intent trigger list and then provide the essential follow-up workflow. Every clause earns its place: the use cases, the free/no-account note, and the upload-then-poll sequence.

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 rich input schema, the output schema, and simple annotations, the description covers the full calling flow: what to ask the user for, what the tool returns, and what to do next. No critical operational detail is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already has 100% description coverage, including detailed enum explanations and guidance for durationSeconds. The tool description adds workflow context but no additional parameter-level meaning, so it does not need to compensate and stays at the baseline.

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 names a clear set of user intents—splitting stems, isolating or removing vocals, making acapella/karaoke, removing noise/reverb—and ties them to this specific tool. It also distinguishes the tool from get_free_audio_job by describing the job-creation and upload workflow rather than the retrieval 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 gives an explicit 'Use this when...' list that covers the major use cases, which is strong contextual guidance. It does not explicitly state when NOT to use it, such as directing users toward create_voice_conversion or create_training_job for those other workflows, but the intent coverage is clear enough to route most calls correctly.

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?

The description clearly discloses that the tool allocates a slot but does not start GPU work until start_training is called, and explains the consent handling behavior. This goes beyond the annotations (readOnlyHint=false, destructiveHint=false) to explain the asynchronous nature and side effects, which is valuable.

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, using three sentences to cover purpose, usage, and behavior. It is front-loaded with the primary action and returns, then adds important caveats. No redundant 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?

Given the complexity (8 parameters, output schema exists), the description adequately covers the essential behavioral aspects like consent and the non-blocking nature. However, with low schema coverage, more detail on parameters like epochs, fileName, and contentType could improve completeness, but the output schema may clarify return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 25%, with most parameters lacking descriptions. However, the description explains the purpose of acceptsStandingAgreement and how it relates to consent, which is critical for correct usage. It also implies the other parameters (fileName, sizeBytes, etc.) are standard metadata, but could benefit from more detail.

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 creates a private NiceVois job and returns a one-time PUT upload URL, distinguishing it from siblings like start_training and list_training_jobs. It specifies the resource (training job) and the action (create), with clear scope (private).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (to create a job before uploading audio) and when not to ask for consent (if standing agreement exists). It also gives a specific flow for handling CONSENT_REQUIRED responses, which is essential for correct 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?

Annotations only declare readOnlyHint=false, openWorldHint=true, and destructiveHint=false. The description adds meaningful behavior beyond these: the tool does not directly return the conversion result but a 'private upload instruction,' and it instructs that only the source vocal should be uploaded. This conveys an upload-then-poll workflow that annotations alone would not reveal. No contradiction with 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?

Three short sentences with the usage context front-loaded before the workflow steps. Every sentence earns its place; no filler. Slightly more could be trimmed, but it is appropriately tight.

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?

An output schema is present, so return values need not be explained in the description. The description covers the essential workflow (private upload instruction, upload source vocal, then call get_voice_conversion with same ID), which is sufficient for an agent to proceed correctly. Minor gap: it does not mention the upload size limit or byte verification that sizeBytes enables, but the schema covers that.

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 5 parameters with definitions (modelId, fileName, sizeBytes, transpose, durationSeconds). The description adds no additional parameter meaning beyond what the schema provides, so the baseline 3 applies.

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 a specific verb (create), resource (voice conversion), and scope (private voice model, one vocal recording). The description also differentiates from get_voice_conversion by framing it as the follow-up call, which helps an agent distinguish the creation step from the retrieval 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?

Explicitly frames the trigger condition ('when the user has selected a private voice model and wants to convert one vocal recording') and lays out the required follow-up sequence (upload source vocal, then call get_voice_conversion). It does not name explicit exclusions or alternative tools, but the stated context is clear enough to route an agent.

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?

The description adds valuable behavioral context beyond the annotations: it states that the tool returns a one-time PUT upload instruction, implying a temporary upload URL and requiring an external upload step. It also hints at the tool being non-mutating (it only prepares) and non-destructive, aligning with the annotations (readOnlyHint=false, destructiveHint=false). However, it does not detail authentication requirements or what happens if the upload is not completed, which would be extra context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the primary purpose and usage trigger, and clearly explains the return and next step. It contains zero filler and every sentence adds value, making it an excellent example of conciseness and structure.

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 presence of an output schema (which likely describes the PUT upload instruction) and the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false), the description is fairly complete. It explains the purpose, the trigger, and the follow-up action. However, it could briefly mention that the upload URL is temporary or that the tool does not actually upload the file, which would improve completeness, but the current description covers the essential 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?

The schema already provides clear descriptions for both parameters: fileName as the original .pth or .zip filename, and sizeBytes with a max limit. The description adds no additional parameter-specific meaning beyond what the schema states, and with schema coverage at 50% (the schema describes both parameters but may not cover all aspects like encoding or extensions), the 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's purpose: to prepare a private RVC model upload, specifying what the user wants (convert with an existing private RVC .pth file or ZIP) and that it returns a one-time PUT upload instruction. This distinguishes it from siblings like creating a training job or converting with a library voice, and it is consistent with the name.

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 states when to use this tool ('when the user wants to convert with an existing private RVC .pth file or complete model ZIP instead of a voice trained in their NiceVois library') and provides a clear next step ('After uploading, call list_voice_models to select the imported voice'). This provides strong usage guidance and names an alternative action (using a library voice) and a follow-up action.

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?

The description reveals a non-obvious side effect: each download consumes source-audio duration from the account's conversion-download minutes. It also discloses the duplicate-file behavior and the need to set confirmRepeat true, which is meaningful behavioral context beyond the 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?

Three concise sentences, each carrying necessary information: usage precondition, billing side effect, and duplicate retry rule. No redundant or filler content.

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 download operation with an output schema and annotations already present, the description covers the key context: when to call, what cost is incurred, and how to handle duplicate downloads. It provides enough behavioral and procedural context for an agent to invoke 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?

The schema already describes kind and confirmRepeat, and the description reinforces confirmRepeat's purpose. However, with only 50% schema description coverage, conversionId and eventId remain largely undocumented by both the schema and the description. The description adds some value but does not fully compensate for those 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 states a specific verb ('Download'), a specific resource ('completed voice conversion output'), and the exact trigger condition ('Use after get_voice_conversion says the job is complete'). This makes the tool's purpose distinct from the sibling creation and status-checking tools.

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 gives clear when-to-use guidance: only after the job is complete and the user wants cleaned or standard output. It also specifies the duplicate-download protocol. It does not explicitly name an alternative to use when the job is still running, but that is strongly implied by the precondition.

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 already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral context by listing what the tool returns: input formats, model choices, workflow, free-beta policy, and outputs. It does not contradict the annotations or imply 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 two sentences, front-loaded with the trigger condition and followed by a concise list of deliverables. Every sentence earns its place with no filler or 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?

Given zero parameters, a read-only annotation profile, and a provided output schema, the description covers the tool's purpose and return scope sufficiently. It is complete for an informational requirements tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there is no parameter burden for the description to carry. The schema is empty and fully covered, and the description appropriately focuses on use case and return content rather than 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 opens with 'Use this when...' and names the exact scenarios: RVC vocal conversion or comparing normal RVC with NiceVois-cleaned results. It then enumerates the returned content, making the tool's purpose and scope clear and distinguishing it from sibling creation/conversion tools.

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 states when to use the tool: when a user wants to convert vocals with RVC or compare RVC results with NiceVois. It provides clear context but does not mention when not to use it or name alternative tools, so it stops short of a 5.

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 already mark this as read-only and non-destructive. The description adds meaningful behavioral context: it is safe to call repeatedly, must respect a 10-second polling interval, and only returns output files once the job completes. It does not detail failure responses, but this is a minor gap given the output schema exists.

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 tight sentences with no filler. It front-loads the critical usage timing ('after create_free_audio_job'), then gives the rate limit, stopping condition, and return content in a logical order.

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 single-parameter polling tool with an output schema and read-only annotations, the description is nearly complete. It covers when to call, how often, when to stop, and what the successful response contains. The only missing detail is what a failed job returns, but this is likely in the 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?

The schema already documents jobId as 'The id returned by create_free_audio_job' with full coverage. The description reinforces this by mentioning create_free_audio_job, but adds no meaningful parameter semantics beyond what the schema 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 states a specific verb ('check'), a clear resource ('free audio job'), and the concrete outcome (lists output files with download links). It distinguishes itself from sibling polling tools by explicitly tying to create_free_audio_job and the free-audio pipeline.

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 gives explicit when-to-use guidance: after create_free_audio_job and upload, with a rate limit ('no more than every 10 seconds'), and a stopping condition ('until the job is completed or failed'). This leaves no ambiguity about the intended polling pattern.

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 already declare readOnlyHint and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond that: it explains the account-scoped nature of the call and the private browser link that preserves agent identity through checkout, which is a meaningful side effect an agent needs to know about.

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 no filler: the usage precondition is front-loaded, and the returned data is enumerated without redundancy. Every clause 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 an output schema present, return-field details are already structured. The description covers when to invoke it, what to expect, and the identity-preserving checkout-link behavior. Nothing an agent needs to call it correctly 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 tool has zero parameters, so there is no parameter syntax to document. The description clarifies that it operates on the connected account's identity, which is useful context and merits the baseline 4 for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific purpose: a preflight check of the connected account's training balance and credits, returning welcome-credit status, available/reserved balance, balance packs, and a checkout link. It clearly distinguishes this account-level balance tool from siblings like get_training_job, which operate on individual jobs.

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 tells the agent when to call this tool — before quoting or creating a training job — which is clear, actionable guidance. However, it does not name alternatives or state when not to use it (e.g., when checking an existing job's status).

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 already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by explicitly stating it is for a 'private' training job and that the tool is meant for polling (implying it is non-mutating and safe to call repeatedly, with a rate hint). No contradictions found.

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 with zero waste. All information is relevant and front-loaded: first sentence states the tool's purpose and scope, second adds a crucial usage constraint. 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?

Given it has an output schema (so return structure is documented), only 1 required parameter with clear purpose, and annotations already cover safety, the description covers all necessary ground. It explains what the tool does, how to use it (polling with rate limit), and distinguishes it from siblings. No gaps remain.

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 0%, meaning the description must compensate. It does not describe the 'jobId' parameter explicitly, but the tool's purpose ('check one training job') strongly implies jobId identifies which job. With only one required parameter, the context is sufficient. A brief clarification of what jobId looks like would merit a 5.

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 ('check'), states a clear resource ('one private NiceVois training job'), and explicitly lists what it retrieves ('stage, completed epochs, ETA, and artifact readiness'). This effectively distinguishes it from sibling tools like create_training_job or list_training_jobs.

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 this tool ('to check one training job') and provides proactive usage guidance ('poll reasonably; do not call more often than every 15 seconds'). This helps the agent avoid misuse or excessive calls.

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 already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context by enumerating exactly what information the tool confirms, including consent requirements and retention, which goes beyond the 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 a single, well-structured sentence that front-loads the usage instruction ('Use this before training') and then efficiently lists the key requirement categories. Every phrase adds value 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?

For a zero-parameter, read-only requirements tool with an output schema, the description is complete. It tells the agent when to use it, what to expect (formats, limits, consent, outputs, retention, upload workflow), and implicitly distinguishes it from action-oriented sibling tools.

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, so the baseline is 4. There are no parameter semantics to explain, and the description appropriately focuses on the tool's informational output rather than inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to retrieve RVC training requirements before training. It lists specific content areas (input formats, duration/epoch limits, consent, outputs, retention, upload workflow), which distinguishes it from sibling tools like create_training_job or start_training that perform actions rather than provide requirements.

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 says 'Use this before training an AI voice,' giving clear temporal guidance for when to invoke the tool. It does not explicitly name alternatives or exclusions, but the pre-training context is strong enough to guide selection among the 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 annotations already marking this as read-only and non-destructive, the description adds meaningful behavior beyond that: it discloses polling cadence, instructs the agent to report the actual returned stage without inventing progress, and prescribes output ordering. These are non-obvious behaviors not available from annotations or 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?

Three short sentences, each earning its place: one sets the use case, one sets polling and accuracy expectations, one prescribes the post-completion action. The most important instruction is front-loaded, and there is no 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?

The task is a simple read/status check with a single parameter. An output schema is present, so return-value documentation is already covered. The description adds all necessary operational guidance: polling limit, honest reporting, and the correct downstream tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage and the prose does not mention conversionId, its origin, or how it relates to create_voice_conversion. Though the tool name and the word 'conversion' hint at the identifier's meaning, the description provides almost no value beyond the schema's property name and constraints.

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 names a specific verb ('check') and resource ('one private NiceVois conversion'), and the title reinforces 'Check an RVC vocal conversion.' It clearly separates this polling/status tool from related creation and download operations, so an agent knows what it does without inspecting the schema.

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 gives explicit when-to-use context ('Use this when checking') and a hard usage rule ('Poll no more than once every 10 seconds'). It also names the companion tool to use after completion, download_voice_conversion_output, providing a clear alternative and handoff.

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 already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful context: no account is needed, and the response includes tool IDs, accepted formats, and a website link. This goes beyond the annotations by clarifying authentication expectations and return payload relevance.

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 primary use ('Use this to see...'), followed by a compact capability list and return-value summary. Every sentence adds value, and there is no redundant or filler content.

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 no-parameter read-only tool with an output schema and clear annotations, the description is fully sufficient. It explains what the tool does, what authentication is needed, what the response contains, and how to use the result in practice.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and 100% schema description coverage, so the baseline is 4. The description appropriately focuses on return content rather than parameters, and no parameter ambiguity exists.

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 ('list') and resource ('NiceVois's free audio tools') and enumerates the exact tool capabilities, making the purpose unmistakable. It also clearly distinguishes itself from sibling creation and job-management tools by focusing solely on listing available free tools.

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 gives clear usage context: use it to see free tools that require no account, and use the returned website link when the assistant cannot upload a file itself. It does not explicitly name alternatives or exclusions, but the 'no account needed' and link-giving purpose provide strong situational 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is safe. The description adds that it lists only 'recent' jobs and for the 'authenticated user', which is useful but not extensive. No additional behavioral details like pagination or sorting are provided.

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 that is front-loaded with the action and resource, and every word adds meaning. No redundant or filler content.

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 no parameters, clear annotations, and an output schema that describes return values, the description covers the essential purpose and usage guidance. It could mention that it only lists recent jobs, but the output schema likely defines the structure. Overall, it's complete for this simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no parameters, and schema description coverage is 100% (trivially). The description adds context by specifying 'recent' and 'authenticated user's', which clarifies the scope implied by the empty schema. A score of 4 reflects that the description effectively handles the parameter-free case.

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 'private RVC training jobs', and specifies it returns the authenticated user's recent jobs. It distinguishes itself from siblings like 'get_training_job' which likely retrieves a single job by ID.

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 states to use this tool before asking for a job ID, providing clear use-case context. However, it doesn't explicitly mention when not to use it or compare directly with siblings like 'get_training_job' or 'start_training'.

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 already declare readOnlyHint=true and destructiveHint=false, so the readonly nature is covered. The description adds transparent context about what the tool returns access to: the user's private NiceVois-trained or uploaded RVC voices, and signals that calling it is a prerequisite before conversion. This is meaningful beyond the structured 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 two sentences with no filler. It front-loads the primary use case and immediately follows with an actionable constraint, making the tool's purpose and invocation behavior easy to absorb.

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 zero parameters, an output schema, and read-only annotations, the description provides the necessary context for a simple list-and-select tool. It states when to invoke it, what it exposes, and a behavioral rule that prevents the agent from prompting for unnecessary input. No critical guidance 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 tool has zero parameters, so there is little parameter-specific semantics to add. The description reinforces the zero-input expectation by instructing the agent not to ask for a model ID first, which compensates for any potential ambiguity about required inputs.

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 names a specific verb (list/select) and resource (private RVC voice models), and clarifies the exact use case: choosing one of the user's private NiceVois-trained or uploaded RVC voices for conversion. This clearly distinguishes it from sibling tools like create_voice_conversion or list_training_jobs without needing to inspect schemas.

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 states the condition for use: 'when the user wants to convert audio and you need to select one of their private NiceVois-trained or uploaded RVC voices.' It also gives a concrete behavior rule: do not ask for a model ID before calling. It lacks explicit when-not-to-use or alternative tool routing, but the clear trigger instruction is strong.

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 already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read operation. The description adds useful behavioral context by listing the specific outputs (payable amount, balance, welcome-credit effect, shortfall, feasibility, private link) and the condition for the link (when more balance is needed). This goes beyond the annotation basics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the critical usage timing ('Call after inspecting the audio and before creating a job') and then lists the returned items. There is zero fluff; every word 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?

Given that an output schema exists (as indicated in context signals), the description does not need to detail the return format. It covers the key usage context, the timing, the outputs, and the conditional private link. It feels complete for the tool's simple purpose, though it could benefit from a note on parameter relevance.

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 schema description coverage at 0%, the description must compensate, but it does not explain what 'epochs' and 'durationSeconds' mean or how they influence the quote. The tool name and context imply they are training parameters, but no explicit semantics are given. This is a significant gap for an agent to correctly construct the inputs.

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 states a clear specific action: quote an RVC training job. It explicitly names the verb 'Quote' and the resource 'RVC training job', and distinguishes it from siblings like create_training_job by saying it returns the payable amount and feasibility before creation. The mention of 'exact payable amount' and 'feasibility' makes the 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 provides a clear usage context: 'Call after inspecting the audio and before creating a job.' This tells the agent when in the workflow to invoke it, but does not explicitly name alternatives or conditions when not to use it. Sibling tools like get_training_requirements exist, but no exclusion 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?

Annotations already indicate write operation (readOnlyHint=false) and non-destructiveness. The description adds the context of starting training after upload, but does not disclose other behaviors like asynchronicity, potential long execution time, response format, or error conditions. It provides adequate but not rich behavioral context beyond 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 a single sentence, which is concise, but it omits necessary parameter explanation and behavioral details. Conciseness is valued, but not at the cost of essential information. It could be slightly expanded without losing efficiency.

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 only one parameter and an output schema that is not described, the description should cover prerequisites (mentioned), response semantics (missing), error possibilities, and async behavior. The context of starting a training job implies possible long-running or queued execution, but no such detail is provided. The description is insufficient for complete 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?

The schema description coverage is 0%, so the description carries the full burden of explaining the only parameter (jobId). However, the description never explicitly states that jobId is the ID returned by create_training_job. It only implies this by referencing the upload URL from create_training_job. An agent would benefit from a clear statement like 'The jobId returned by create_training_job.' This omission fails to add meaning beyond the bare 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 ('Starts') and the resource ('the private cloud RVC training job'), distinguishing it from sibling tools like create_training_job (which prepares the job) and get_training_job (which checks status). It is specific and immediately informative.

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 says 'Use this only after the source audio was successfully PUT to the upload URL returned by create_training_job,' providing a clear precondition and referencing the correct sibling tool. This tells the agent exactly when and after what step to invoke this tool.

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