look_at_image

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

With no annotations provided, the description carries the behavioral burden. It explains that the tool accepts local paths or URLs, returns a text description, and that the prompt parameter focuses attention. It does not cover error cases or edge limitations, but provides solid transparency for a vision interpretation tool.

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 front-loaded with a mandatory-call marker and purpose, then dives into clear parameter explanations. Every sentence adds value, and the structure is easy to scan.

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 the core use cases, parameter semantics, and behavioral expectations. It could be more complete by explicitly addressing sibling tool differentiation and error/limitation scenarios, but it is strong for a moderately simple tool with an output schema.

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 0%, and the description fully compensates. It explains the image parameter with absolute path examples and URL support, and describes the prompt parameter's default behavior. This goes well beyond the bare schema definition.

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: understand an image and return a text description. It distinguishes itself from the sibling extract_text_from_image by focusing on content understanding rather than text extraction, though it does not explicitly name the sibling.

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 explicit when-to-use guidance: when the user uploads/pastes an image or mentions a file path, and even instructs the agent not to refuse. However, it lacks when-not-to-use guidance or explicit mention of the alternative extract_text_from_image.

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