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 usefully enumerates exactly what subsystems are probed, which is real behavioral disclosure, and '检查' implies a read-only operation, but it says nothing about return format, latency, or whether checks fail gracefully.

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 compact sentence that front-loads the verb and lists the checked components with 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?

For a zero-parameter diagnostic with no output schema, the description covers what is inspected. The remaining gap is that the shape of the health report is undocumented, but that is a minor omission for a tool this simple.

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 takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate.

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 (检查 / check) plus four concrete resources: local model, FFmpeg, VoxCPM2 service, and GPU VRAM. It is unmistakably a diagnostic tool versus the speech-oriented siblings, though it does not name any sibling explicitly.

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: an agent can infer this is a pre-flight/diagnostic check to run before calling speak or sing_* tools. There is no explicit 'use when' statement, no conditions, and no mention of 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?

There are no annotations, so the description carries the full behavioral burden, yet it says nothing about that this is a local model inference (GPU/CPU), that it can be slow, whether output is overwritten, or how the produced audio is returned. It only reveals the underlying model name and the metadata precondition.

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?

One sentence with no filler, so it is concise, but it is concise to the point of under-specification. For a tool with 9 parameters and no schema documentation, that brevity is a liability rather than a virtue.

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, no annotations, no output schema, and zero schema descriptions, the one-line description falls well short. An agent would not know how to set control or whether a prompt is required to clone a voice.

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 9 parameters, but none have descriptions and the coverage is 0%; the description only implicitly references target_metadata_path. Important parameters such as control (score vs melody), prompt_wav_path/prompt_metadata_path (voice cloning references), pitch_shift, and output_format are left entirely unexplained.

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 and resource: generate melodic singing (带旋律的歌声) from pre-written target metadata using SoulX-Singer. An agent can distinguish this task from speak/speak_segments (speech) and infer a difference from sing_notes, though it never explicitly names or contrasts with those 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?

No guidance on when to choose sing_metadata over sing_notes, no prerequisites for what 'pre-written target metadata' means or how to obtain it, and no mention of when to use a prompt WAV/metadata. The only implied condition is that metadata must already exist.

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, yet it discloses almost nothing beyond 'this produces singing'. It says nothing about what audio is returned, how the device/prompt WAV affect execution, whether generation is expensive or requires a model download, or what happens when the three arrays have mismatched lengths.

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 filler, and the key inputs are stated before the 'no metadata needed' benefit. It is efficient, though the terseness borders on under-specification for a 12-parameter tool.

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, zero schema description coverage, no annotations, and no output schema, the definition leaves an agent unable to determine array alignment rules, default behavior of auto_shift/pitch_shift, or what the tool returns. The one-sentence description is far too thin for this complexity.

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 0% across 12 parameters. The description only names the three required ones (phonemes, note_pitches, durations_ms) without explaining units, alignment, or length constraints, and the nine optional parameters — text, device, language, auto_shift, pitch_shift, output_format, prompt_wav_path, prompt_metadata_path, normalize_loudness — are entirely undocumented in both schema and description.

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 action (generate singing) and the three inputs that drive it (phonemes, MIDI pitches, per-character durations), so the purpose is unmistakable. The clause '不需要手写完整元数据' implicitly distinguishes it from the sing_metadata sibling, though it never names that sibling outright.

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 implied: use this when you already have phonemes/pitches/durations and want to skip writing full metadata. However, it never explicitly says when to prefer sing_metadata or speak/speak_segments instead, nor does it state prerequisites for the prompt_wav_path and prompt_metadata_path options.

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. It discloses the engine (local VoxCPM2) and that the result is a file path, which is useful, but says nothing about voice cloning via a reference audio, latency/cost of local inference, whether output files are overwritten, or how the control instruction actually steers emotion.

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 purpose then return value; no filler. It is efficient, though the brevity edges into under-specification given the parameter surface.

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?

Eight parameters, zero annotation coverage, and no output schema, yet the description explains neither the knobs nor the output beyond 'a file path'. For a TTS tool with a voice-reference parameter and a tunable control instruction, this is well short of what an agent needs to call it 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 0% across 8 parameters, and the description mentions none of them. The terms '情感旁白' hint that control_instruction drives emotional delivery, but seed, cfg_value, inference_timesteps, normalize_loudness, and reference_audio_path (a high-impact voice-cloning input) are left entirely opaque.

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 (generate speech / emotional narration with local VoxCPM2) plus the return artifact (audio file path). It implicitly contrasts with the singing siblings (sing_notes, sing_metadata) by naming narration/speech, but never explicitly distinguishes itself from speak_segments.

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 pick this over speak_segments, when a reference voice should be used, or any prerequisites (model availability, GPU, language support). The agent must infer the single-utterance vs. segmented distinction from sibling names alone.

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 does disclose the two-stage behavior (per-segment synthesis, then pause-based concatenation into one track), but says nothing about the return artifact (file path vs. audio payload), latency/model cost, or side effects of a 7-parameter generation call.

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 compact sentence with the action and composition behavior front-loaded and no filler. It is efficient, though the brevity comes at the cost of the missing information noted elsewhere.

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 7-parameter generation tool with no annotations and no output schema, the definition is too thin: nothing about what is returned, whether a reference voice is required, or what the numeric knobs change. An agent could call it, but not confidently or optimally.

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 reported at 0% for the top-level parameters: seed, cfg_value, output_format, normalize_loudness, inference_timesteps, and reference_audio_path are undocumented in both schema and description. The phrase '按停顿拼接' hints at the per-segment pause_ms, but the description explains none of the remaining six parameters or how reference audio affects the output.

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+resource: synthesize each of several text segments with VoxCPM2 and concatenate them by pause into one emotional narration. This clearly separates it in spirit from the single-utterance sibling 'speak', but it never names that sibling or explicitly states the boundary.

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?

'多段文字' implies the tool is for multi-segment narration, so the when-to-use is inferable, but there is no explicit guidance to prefer 'speak' for a single utterance, no prerequisites, and no mention of the conditions under which segmentation is warranted.

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