CleanAudio

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

With no annotations the description carries the full burden, and it discloses almost no behavior beyond the operation itself: no processing time, async-vs-sync, whether the URL must be publicly reachable, size/length limits, or what comes back (no output schema exists). The one useful hint is the accepted input forms, which is thin for a processing 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?

Two short sentences, front-loaded with the operation and finished with the input forms. No filler, and each sentence carries information the agent needs.

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 4-parameter tool with no annotations and no output schema, the definition leaves real gaps: it does not state that at least one of audio_url/audio_base64 must be supplied (required list is empty and additionalProperties is false), nor how the cleaned audio is returned. Adequate to call, but incomplete for correct invocation and follow-up handling.

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 filename, strength, audio_url, and audio_base64 in detail. The description restates only the URL/base64 inputs and adds nothing about strength semantics or the mutual-exclusivity of the two audio sources, 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 ('Remove background noise') and resource (audio) plus the effect ('improve speech clarity'), and names the engine (CleanAudio AI). It is distinguishable from siblings like remove_silence or transcribe, though it never explicitly contrasts itself with 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?

Usage is only implied: the agent can infer this is for noisy recordings that need cleanup. There is no explicit when-to-use/when-not, no mention of remove_silence as an alternative, and no prerequisites stated.

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 behavioral burden. It states that it returns clipped audio and uses seconds, but does not disclose permissions, input-source handling, whether output overwrites a file or creates a new one, format/size limits, or error behavior for a media-processing 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 two-sentence description is front-loaded with the action and scope, and every sentence earns its place. There is no redundant or filler text.

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 five parameters, no annotations, and no output schema, yet the description never mentions the audio source parameters or how URL, base64, and filename interact. It also omits format and size constraints, so it is not complete enough for an agent to invoke the tool confidently without reading the 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 description coverage is 100%, so the schema already documents start, end, filename, audio_url, and audio_base64. The description only echoes start/end semantics in seconds and adds no guidance on choosing between audio_url and audio_base64, so the baseline score of 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?

The description uses a specific verb ('Trim') and resource ('audio file'), states the start/end scope in seconds, and mentions the return value. It does not explicitly distinguish this from audio-processing siblings such as remove_silence or join_audio, but the operation itself is clear.

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?

There is no guidance on when to use this tool versus clean_noise, remove_silence, join_audio, or transcribe. The clipping use case is only implied by the verb 'Trim', leaving the agent to infer 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 the full behavioral burden, and it discloses only the output format and ordering. It says nothing about URL accessibility requirements, mixed-format/sample-rate handling, failure behavior on a bad URL, or how the resulting MP3 is delivered back.

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 with zero waste and the output format stated early. It is efficient, though its brevity leaves little room for the behavioral detail an unannotated tool needs.

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 no annotations and no output schema, the description should explain what comes back (a URL, a job id, binary data) and any constraints on the inputs. It covers only the core operation, leaving the agent guessing about the response contract.

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% and the parameter already documents the ordered 2-10 URL list, so the schema does the heavy lifting. The description reinforces that order is preserved ('in the given order'), which adds slight emphasis but no new syntax or constraint 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 gives a specific verb (concatenate) and resource (audio files) and states the output format (one MP3) plus ordering semantics. It does not explicitly distinguish itself from siblings like cut_audio or remove_silence, though the operations are inherently distinct.

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?

Usage is only implied — the agent can infer this is for combining multiple clips into a single file. There is no explicit statement of when to prefer this over edit-type siblings, no prerequisites, and no exclusions.

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 full burden and discloses almost nothing: it does not say whether the original file is overwritten, whether output is a new file or URL, how aggressive the removal is, or whether processing is async. 'Hint for the AI cleaner' in the schema even suggests an LLM-driven, nondeterministic step, which the description never surfaces.

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 with the action and resource, with zero filler. Nothing is padded or restated from the tool name.

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 media-mutating tool with no annotations and no output schema, the description leaves critical gaps: return value/shape, whether sources are modified in place, and behavior when no silence is found. The rich schema covers inputs, but the agent still lacks enough to invoke confidently.

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%, with each of the four parameters documented in the schema (threshold dB default, URL vs base64 input, filename hint). The description adds no additional meaning about how these interact or which input form is preferred, 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 ('remove') and resource ('long silences / dead air') plus the intent ('tighten pacing'), so the transformation is unambiguous. It does not explicitly distinguish itself from siblings like cut_audio or clean_noise, which is the only thing keeping it from a 5.

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

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

The phrase 'to tighten pacing' hints at intent but gives no conditions for use, no prerequisites, and no comparison to the closely related siblings cut_audio and clean_noise. An agent must guess which of these three trimming tools applies.

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 behavioral burden and falls short: it does not say what is returned (audio file path, bytes, URL?), where the audio is written, or any rate/length constraints beyond the schema's 5000-char cap. For a generation tool with no output schema, this leaves a significant gap.

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 with no wasted words. It is efficient, though its brevity is part of why behavioral detail is missing rather than a sign of completeness.

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 no annotations and no output schema, the description must explain what calling the tool produces and how the caller retrieves the audio. It does neither, so an agent cannot predict the result of 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?

Schema description coverage is 100%, documenting both `text` (max 5000 chars) and `voice` (default en-US-JennyNeural), so baseline 3 applies. The mention of 'Edge TTS voices' hints that voice ids follow Edge conventions, but no examples or enumeration are added.

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 and resource (convert text -> natural speech audio) plus the underlying engine (Edge TTS via CleanAudio). It is obviously distinct from the audio-editing siblings (clean_noise, cut_audio, transcribe), though it never explicitly contrasts itself with 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?

There is no guidance on when to use this versus the sibling tools (e.g. transcribe for the reverse direction) and no stated preconditions. Usage is only implied by the tool name and the phrase 'Edge TTS voices'.

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 at all, the description carries the full disclosure burden but says nothing about authentication, size/duration limits on audio, supported formats, latency, cost, or what the response contains. A mutation-free read operation is safe to assume, but an agent gets nothing about constraints or failure modes, and there is no output schema to compensate.

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 naming the action, input, output, and engine with no filler. It is efficient, though it errs toward under-specification rather than over-verbosity.

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 annotations, no output schema, and four optional parameters create a real gap: an agent doesn't know what the return value looks like, whether languages are restricted, whether one of the audio inputs is mandatory, or how large a file can be. The one-line description doesn't fill any of these gaps.

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

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

Schema description coverage is 100%, so filename, language, audio_url, and audio_base64 are already documented. The description adds no parameter meaning beyond that baseline, and notably omits the likely mutual exclusivity of audio_url vs. audio_base64 that the schema also doesn't enforce (required list is empty).

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?

Names a specific verb and resource: transcribe speech in an audio file to text, plus the underlying engine (CleanAudio speech-to-text). It doesn't distinguish itself from sibling tools, notably text_to_speech (the inverse direction), leaving the agent to infer the direction from the name alone.

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?

Usage is only implied by the verb 'transcribe' – there is no explicit when-to-use statement, no mention of when to prefer a sibling like text_to_speech or clean_noise preprocessing, and no guidance on choosing between audio_url and audio_base64 inputs.

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