picdefense_extract_text

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It discloses the truncated flag for text-dense images and the fact that it consumes account credits, which are useful behavioral details. However, it omits error handling, rate limits, or authentication requirements, preventing a perfect score.

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 two sentences, front-loaded with the primary action, and every sentence adds value (return structure, truncation, cost). No redundant or filler 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?

For a one-parameter tool with no output schema, the description thoroughly covers the return values and a key side effect (credit consumption). Minor gaps such as supported image formats or size limits prevent a top score, but overall it is quite complete.

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?

The schema already documents the single 'url' parameter completely (100% schema description coverage), so the description need not add parameter-level detail. It neither adds nor detracts from the schema, so it meets the baseline of 3.

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 extracts text from an image via OCR, and explicitly lists the output components (full text, individual words, truncated flag). This distinguishes it from sibling tools like detect_face or detect_labels, which serve different image-analysis purposes.

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 use for OCR text extraction, but gives no explicit guidance on when to choose this tool over siblings, nor any exclusions or alternative recommendations. The usage context is clear from the verb 'extract' but not explicitly elaborated.

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