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

With no annotations, the description carries the behavioral disclosure burden. It usefully discloses that local ASR does not consume API quota and that final organization requires a DeepSeek API key. It does not mention failure modes, rate limits, video length constraints, or whether the tool downloads the video, so transparency is partial.

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 with a one-sentence purpose, a parameter list, a return note, and an important API-key note. It is concise and front-loaded, with no significant redundancy, though the return line adds little beyond the output schema.

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 covers inputs, key dependencies, and a basic return value, which is enough for simple invocations. However, it lacks guidance on choosing between sibling tools, expected output format details, and operational constraints, so completeness is only moderate.

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%, but the description compensates by explaining all three parameters: share_link can contain the link or surrounding text, model defaults to medium and is optional, and context defines the organization purpose or extra requirements. It adds meaningful semantics beyond the bare schema, though model choices and context format remain underspecified.

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 action: extracting text content from a Douyin video via a share link. It implies differentiation from siblings like get_douyin_download_link and recognize_audio_file/url by targeting share links and extracted text, though it does not explicitly name those alternatives.

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 Douyin share link is provided and text/口播 content is desired, and notes when DOUYIN_DEEPSEEK_API_KEY is required. However, it does not explicitly state when to prefer this tool over siblings such as recognize_audio_file, recognize_audio_url, or parse_douyin_video_info.

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 does disclose the return format (JSON string with download link and video info) and the watermark-free promise, but it omits any caveats about invalid links, request failures, or underlying network behavior. That is a moderate but not complete level of 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 tightly written: a one-line purpose, a one-line parameter explanation, and a one-line return summary. No filler or repetition is present, and the key information is 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?

The tool is simple and an output schema exists, so the return contract is already defined. However, the description does not explain how this download-link tool relates to siblings or what preconditions apply to the share_link, leaving some context gaps for an agent choosing among similar 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 schema only provides the parameter name and type, while the description adds important meaning: the share_link can be a share link or any text containing the link. This materially helps the agent construct a valid invocation and justifies a score above the 0%-coverage 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 states a specific verb ('获取'/fetch) and a clear resource ('抖音视频的无水印下载链接'/watermark-free Douyin video download link), which differentiates it from siblings that extract text, recognize audio, or parse video info.

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 given about when to prefer this tool over the listed siblings such as parse_douyin_video_info or extract_douyin_text. The description only says what the tool does and the required parameter, leaving the selection decision entirely to inference.

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 only states the return format (JSON string) and omits network dependency, error behavior, rate limits, link-expiration handling, or behavior for invalid/ambiguous inputs.

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 compact and well-organized into purpose, parameters, and return sections. Every sentence adds value, and the main action is front-loaded with no redundant 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 single-parameter parsing tool with an output schema available, the description covers the essential input and return contract. However, it lacks behavioral context such as error handling, network requirements, and link validity assumptions, making it only minimally 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 description coverage is 0%, but the description compensates by explaining that share_link can be either a Douyin share link or text containing a link. This adds meaningful semantics beyond the bare schema property title 'Share Link' and clearly documents the only parameter.

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 (parse a Douyin share link) and the outcome (retrieve basic video information), which distinguishes it from the download/text/audio sibling tools by resource and purpose. However, it does not explicitly contrast itself with those siblings, so it stops short of 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 intended use is implied: when the agent receives a Douyin share link and needs basic video info, this tool is appropriate. But there is no explicit guidance about when not to use it or when to prefer siblings like get_douyin_download_link or extract_douyin_text.

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 behavioral burden and delivers: it discloses local inference via faster-whisper, the default 'medium / CPU int8' engine, and zero API consumption. A minor flaw is the internal inconsistency between the model parameter's stated default 'qwen3-asr-flash' and the faster-whisper medium default, but the core safety/cost behavior is clearly revealed.

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 cleanly organized—purpose, parameters, returns, and a behavioral note—with the purpose statement front-loaded. The parameter block earns its place because the schema has no descriptions at all, so it is not redundant, though the layout is conventional rather than exceptionally 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?

For a 4-parameter tool with no annotations, the description is largely complete: it covers purpose, all parameters, the return value ('识别的文本内容'), and operational context in one place. It omits supported audio formats, file-size/duration limits, and error behavior, which would round it out for a local ASR 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 0%, and the description fully compensates by documenting every parameter: file_path as a local path, context as an accuracy-improving optional text, language with 'zh'/'en' examples and auto-detection default, and model with its default. This is exactly the semantic meaning the bare input schema lacks.

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 opening line '识别本地音频文件中的文本' states a specific verb (识别/recognize), a resource (audio files), and a scope qualifier (本地/local), so an agent knows immediately this transcribes local audio. The '本地' qualifier also distinguishes it from the sibling recognize_audio_url, which targets remote audio.

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: it operates on local file paths via '本机 faster-whisper' with CPU int8 and notes '不消耗 API', signaling an offline, no-cost transcription use case. However, it never explicitly names the alternative (recognize_audio_url) or states when not to use this tool, leaving sibling routing to inference.

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 behavior disclosure burden. It discloses the DASHSCOPE_API_KEY environment variable requirement, the default model, the auto-detect behavior for language, and the return type. It stops short of mentioning external data transmission, rate limits, or failure behavior, so it isn't perfect.

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 concise structured list with a one-line purpose, parameter definitions, return value, and a single operational note. No sentence is wasted, and the most important info is 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?

Given the output schema and the description, the agent knows all parameters, defaults, the required API key, and what is returned. It lacks explicit usage-contrast guidance and format constraints for the URL, but those are minor for a straightforward transcription 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 0%, and the description compensates by explaining every parameter: audio_url is the link, context is optional textual context for improved accuracy, language accepts codes like 'zh'/'en', and model defaults to qwen3-asr-flash. The defaults and optionality are explicitly stated.

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 the verb 'recognize' and resource 'online audio URL' and result 'text' in the first line, making the tool's purpose unambiguous. It is naturally distinguished from sibling recognize_audio_file because it targets URLs rather than files.

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 the tool is for online audio URLs through its name and opening phrase, but does not explicitly contrast it with the sibling recognize_audio_file or other alternatives. There is no 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.