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

Annotations already provide readOnlyHint and openWorldHint. The description adds specific behavioral details: the exact model used (doubao-seed-2.0-pro), that it requires absolute paths or URLs, and limitations (no video/non-images). This goes beyond annotations without contradiction.

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 (5 short sentences) with front-loaded purpose. Every sentence adds information: model, supported inputs, return, use cases, and limitations. No redundant or filler content.

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 only 2 parameters with full schema coverage, no output schema, and annotations present, the description covers all key aspects: functionality, input formats, return, model, and limitations. It could mention output details (e.g., JSON structure) but is adequate for its simplicity.

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% with parameter descriptions. The tool description adds extra semantics for the 'prompt' parameter, explaining that leaving it blank uses a default analysis framework and that filling it appends to the prompt and guides output structure. This adds value beyond the schema alone.

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 uses a specific visual model to recognize image content. It specifies supported input types (local path and URL), return format (text description), and use cases. It also explicitly states what it does not support (video/non-image files), effectively distinguishing the tool even without 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?

The description provides when-to-use context by listing use cases like object recognition and text extraction, and explicitly states non-support for video/files. However, it lacks explicit guidance on when to prefer this tool over alternatives, and there are no sibling tools to compare.

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