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 uploading and extracting content but lacks details on permissions needed, rate limits, error handling, or what 'processing' entails beyond extraction. For a tool that interacts with files and an AI model (GLM-4.5V), this is a significant gap in 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 concise and front-loaded, stating the core purpose in the first part. It efficiently lists supported formats without unnecessary elaboration. However, it could be slightly improved by structuring usage guidelines or behavioral details, but it avoids redundancy and waste.

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 of file processing with an AI model (GLM-4.5V), no annotations, and no output schema, the description is incomplete. It doesn't cover what the tool returns (e.g., extracted text, structured data), error cases, or operational constraints. For a tool with potential side effects (uploading files), more context is needed.

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 both parameters ('filePath' and 'extractPrompt') with descriptions. The description adds minimal value beyond the schema by implying file processing and extraction, but it doesn't provide additional syntax, format details, or examples for parameters. Baseline 3 is appropriate when the schema does the heavy lifting.

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: '使用 GLM-4.5V 处理文件（上传并提取内容）' - it processes files by uploading and extracting content using GLM-4.5V. It specifies supported formats (PDF, DOCX, etc.), making the purpose concrete. However, it doesn't explicitly differentiate from sibling tools like 'read_image' or 'vision_query', which might have overlapping functionality for image processing.

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 lists supported formats but doesn't explain scenarios where this tool is preferred over 'read_image' (which might handle images) or 'vision_query' (which might involve visual queries). There's no mention of prerequisites, limitations, or comparative use cases.

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 full burden. It states the tool reads images and returns dataURL with dimensions, but lacks details on error handling (e.g., invalid paths, unsupported formats), performance (e.g., size limits, rate limits), or side effects (e.g., caching). This is a significant gap for a tool with no annotation coverage.

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 in Chinese that front-loads the core functionality ('读取本地/URL图片') and specifies the return value ('返回 dataURL 与尺寸信息'). There is zero waste, making it appropriately sized and well-structured.

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 no annotations and no output schema, the description is incomplete. It doesn't explain the return format (e.g., structure of dataURL and dimensions), error cases, or prerequisites (e.g., network access for URLs). For a tool with 2 parameters and no structured output, more context is needed to be fully helpful.

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 both parameters ('path' and 'maxSide'). The description adds no additional meaning beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('读取' meaning 'read') and resource ('图片' meaning 'image'), specifying it reads local or URL images and returns dataURL with dimension information. It distinguishes from sibling 'process_file' and 'vision_query' by focusing on basic image reading rather than processing or querying, though the distinction could be more explicit.

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 like 'process_file' or 'vision_query'. It mentions local/URL sources but doesn't specify scenarios where this tool is preferred over siblings, leaving usage context implied rather than explicit.

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 provided, the description carries the full burden of behavioral disclosure. It mentions the AI model ('GLM-4.5V') and general capabilities but lacks critical details: it doesn't specify output format (though 'returnJson' parameter hints at this), rate limits, authentication needs, error handling, or performance characteristics. The description is too vague for a tool with AI inference capabilities.

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 in Chinese that directly states the tool's purpose. It's front-loaded with the core action and avoids unnecessary words. However, it could be slightly more structured by separating the three capabilities for clarity, but it remains appropriately concise.

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 of an AI vision tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., text, structured data), how to interpret results, or any limitations (e.g., image size, format support). For a 4-parameter tool invoking an AI model, this leaves significant gaps for an agent to use it effectively.

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 fully documents all four parameters. The description adds no parameter-specific information beyond what's in the schema—it doesn't explain the 'mode' enum values or how 'prompt' interacts with different modes. Baseline 3 is appropriate since the schema does the heavy lifting, but the description doesn't compensate with additional context.

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 ('调用 GLM-4.5V 对图片进行') and the resource ('图片'), specifying three capabilities: OCR, Q&A, and detection. It distinguishes from sibling tools like 'process_file' and 'read_image' by focusing on AI-powered analysis rather than basic file operations. However, it doesn't explicitly differentiate between the three modes or mention the default 'describe' mode from 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 provides no guidance on when to use this tool versus alternatives like 'process_file' or 'read_image'. It lists capabilities but doesn't specify scenarios, prerequisites, or exclusions. For example, it doesn't indicate whether it's for complex AI analysis versus simple image reading, leaving usage context entirely implicit.

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