Describe Image

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

No annotations are provided, so the description must disclose behavioral traits. It mentions the use of an 'OpenAI-compatible vision model' but does not discuss potential failure modes, latency, cost implications, or the read-only nature of the tool. The description adds some context beyond the schema but leaves gaps 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 only two sentences long, with no redundant information. Every word adds value, making it highly concise and easy to parse.

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 has no output schema, so the description should explain what the tool returns (e.g., a textual description, analysis results). It does not mention the output at all, leaving a significant gap in completeness. While the input is well-covered, the lack of output context reduces the score.

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 each parameter described in the schema. The description lists the input methods but adds no additional semantic meaning beyond what the schema provides. Therefore, a baseline score of 3 is appropriate.

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: analyzing images with a vision model, and lists the supported input methods (image_path, image_url, image_base64, images[]). This provides a specific verb-resource combination and effectively differentiates the tool from potential alternatives, though no siblings are listed.

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 for image analysis tasks but does not explicitly state when to use this tool versus others or provide any exclusions. With no sibling tools to differentiate, the guidance is adequate but lacks explicit 'when not to use' context.

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