analyze_image

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

There are no annotations, so the description bears the transparency burden. It discloses the vision-model dependency and that output is plain text. However, it does not mention potential limitations, error behavior, or whether any authentication or prerequisites apply, leaving some behavioral uncertainty.

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?

Three short, front-loaded sentences with no filler. It states purpose, use case, and supported modes efficiently. Every sentence earns its place.

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?

For a simple general-purpose vision tool, the description is mostly complete: it names the model limitation, output type, and single/multi-image support. However, it does not explicitly differentiate from specialized siblings or clarify whether a prompt is expected if not provided, leaving minor gaps.

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 baseline is 3. The description adds a small clarification that single vs. multi images are supported via 'image' or 'images', but this mostly repeats what the schema already explains. It does not add meaningful new semantics beyond the schema.

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 a specific verb and resource: 'Analyze one or more images with a vision model and return plain text.' It also distinguishes itself from sibling tools by framing itself as 'general Q&A when the host model cannot see images,' setting it apart from specialized tools like ocr_image or compare_images.

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?

It explicitly says when to use: 'Use for general Q&A when the host model cannot see images.' This gives clear for-use context, but it does not explicitly mention when not to use specialized alternatives (e.g., describe_image, ocr_image), so it earns a 4 rather than a 5.

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