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

With no annotations provided, the description carries full burden for behavioral disclosure. It successfully reveals several important behavioral traits: the tool performs text extraction from video content, requires an API key (DASHSCOPE_API_KEY environment variable), uses speech recognition (implied by the model parameter), and has an optional model parameter with a default. However, it doesn't mention rate limits, error conditions, or authentication details beyond the API key requirement.

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 clear sections (purpose, parameters, returns, notes) and uses only essential sentences. Each section earns its place by providing distinct information. The Chinese text is concise and direct, though the formatting with section headers could be slightly more polished.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, speech recognition functionality) and the presence of an output schema (which handles return values), the description provides good contextual coverage. It explains the core functionality, parameters, authentication requirement, and basic workflow. The main gap is lack of error handling information and more detailed model options.

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 0% schema description coverage, the description must compensate. It provides meaningful semantic context for both parameters: 'share_link' accepts Douyin share links or text containing links, and 'model' specifies the speech recognition model with a default value. This adds substantial value beyond the bare schema, though it could provide more detail about valid model options or link formats.

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 specific action ('从抖音分享链接提取视频中的文本内容' - extract text content from Douyin share links) and the resource (Douyin video content). It distinguishes itself from sibling tools like 'get_douyin_download_link' and 'parse_douyin_video_info' by focusing specifically on text extraction rather than downloading or general video information parsing.

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

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

The description implies usage context through the mention of Douyin share links and the optional model parameter, but doesn't explicitly state when to use this tool versus the sibling tools. There's no guidance about alternative approaches or specific scenarios where this tool is preferred over 'parse_douyin_video_info' which might also provide text information.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool returns '包含下载链接和视频信息的JSON字符串' (JSON string containing download link and video information), it doesn't disclose important behavioral traits like whether this requires authentication, rate limits, error conditions, or what happens with invalid links. The description provides basic output information but lacks crucial operational 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 appropriately sized and well-structured with clear sections for purpose, parameters, and return value. Each sentence earns its place by providing essential information. The Chinese text is direct and avoids unnecessary elaboration, though it could be slightly more front-loaded by stating the core purpose more prominently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single parameter, specific purpose) and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the core functionality, parameter semantics, and output format. The main gap is the lack of behavioral context that would normally come from annotations, but the description provides adequate information for basic usage.

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

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

With 0% schema description coverage, the description must compensate for the lack of parameter documentation in the schema. It successfully adds semantic meaning by explaining that the 'share_link' parameter accepts '抖音分享链接或包含链接的文本' (Douyin share link or text containing a link), which clarifies the parameter's purpose beyond what the bare schema provides. This is valuable context for a single-parameter 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 clearly states the tool's purpose with a specific verb ('获取' meaning 'get') and resource ('抖音视频的无水印下载链接' meaning 'Douyin video watermark-free download link'). It distinguishes from sibling tools like 'extract_douyin_text' and 'parse_douyin_video_info' by focusing specifically on obtaining download links rather than text extraction or video information parsing.

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

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

The description implies usage context through the parameter description ('抖音分享链接或包含链接的文本' meaning 'Douyin share link or text containing a link'), suggesting this tool should be used when you have a share link. However, it doesn't explicitly state when to use this tool versus the sibling tools, nor does it provide any exclusion criteria or alternative scenarios.

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 mentions parsing links and returning JSON-formatted video information, but lacks details on error handling, rate limits, authentication needs, or what specific video fields are included. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.

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 appropriately sized and front-loaded, with a clear purpose statement followed by brief sections for parameters and returns. Each sentence adds value without redundancy. Minor improvements could include bullet points or more structured formatting, but it remains efficient and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose, parameter semantics, and output format. However, it lacks behavioral details like error cases or performance considerations, which would enhance completeness for a tool with no annotations.

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

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

The description adds meaningful context beyond the input schema, which has 0% coverage. It explains that 'share_link' can be a Douyin share link or text containing a link, clarifying the parameter's purpose and acceptable formats. With only one parameter and no schema descriptions, this compensation is effective, though not exhaustive (e.g., no examples or validation rules).

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: '解析抖音分享链接，获取视频基本信息' (Parse Douyin share links to obtain basic video information). It specifies the verb ('解析' - parse) and resource ('抖音分享链接' - Douyin share links), and distinguishes from siblings like 'extract_douyin_text' and 'get_douyin_download_link' by focusing on video metadata rather than text extraction or download links. However, it doesn't explicitly contrast with siblings, keeping it at 4 instead of 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 description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools ('extract_douyin_text' or 'get_douyin_download_link') or specify scenarios where this tool is preferred, such as for metadata retrieval versus content extraction. Usage is implied by the purpose but lacks explicit context or exclusions.

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