analyze_image

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

With no annotations to lean on, the description carries the full burden and does well: it discloses the use of a multimodal model, the text-only nature of the agent, the return of a detailed description, and the effect of 'save_to' (writes to file, returns path + summary). It describes defaults ('detail' defaults to 'high') and source resolution behaviors. It does not cover error cases or permissions, but the disclosed behavior is solid.

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 bullets, front-loads the core purpose in the first sentence, and every subsequent line adds distinct information (sources, tasks, defaults, file output). It is appropriately sized for a tool with 5 parameters and no output schema, with no filler or repetition.

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 complexity (5 parameters, no output schema, no annotations), the description is remarkably complete. It covers all input source types, task modes, prompt override, detail level, and save_to behavior. The only minor omission is exact return formatting, but the description explicitly says 'detailed text description' and 'path + summary' for save_to, which is sufficient for correct invocation.

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%, so baseline is 3, but the description adds substantial meaning beyond the schema. It explains each image source format with examples, declares the precedence between 'prompt' and 'task', clarifies the 'detail' default, and describes the 'save_to' output behavior. This is exactly the kind of added value that helps an agent pick and fill parameters correctly.

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 states a specific verb and resource: 'Analyze an image using a multimodal model and return a detailed text description.' It also explains the motivation (the vision model sees the image, the calling agent is text-only), making the purpose unambiguous and strongly differentiated from any generic tool.

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 clearly explains when to use the tool: whenever a text-only agent needs image understanding. It gives concrete guidance on selecting image sources and tasks, and notes that 'task' is ignored when 'prompt' is provided. No explicit when-not-to-use or alternatives are mentioned, but there are no sibling tools to differentiate from.

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