analyze_image

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

No annotations provided, so description carries full burden. It discloses input flexibility, optional question, requirement for a vision provider, and fallback to metadata+OCR. Does not mention privacy, rate limits, or response structure specifics. Output schema exists so structural detail is not required, but the fallback behavior is well-explained.

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?

Six sentences that front-load the core purpose. Every sentence adds distinct value: purpose, use case, input formats, optional parameter, return type, and fallback behavior. No extraneous 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 zero schema coverage, no annotations, and two parameters (one required), the description covers all essential aspects: input options, optional focus question, dependency on vision provider, fallback behavior. Siblings in context allow differentiation. It is complete for an AI to decide when to invoke.

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?

Input schema has no descriptions (0% coverage). The description adds critical semantics: image parameter accepts multiple formats (local path, URL, data URI, base64). Question parameter is explained with concrete examples (e.g., 'What error is shown?'). This fully compensates for schema gaps.

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 understands an image and returns a detailed text description. It lists accepted formats (local path, URL, data URI, base64) and optional question parameter. This differentiates it from siblings like ocr_image (simple OCR) and web_search (text-based).

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?

Explicitly states when to use: when you need to 'see' an image because the model cannot read images directly. Gives examples for the question parameter. Lacks explicit exclusions or direct comparison to ocr_image, but context from sibling tools provides implicit discrimination.

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