ocr_extract

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

No annotations are provided, so the description must fully disclose behavioral traits. However, it does not mention performance, limitations (e.g., file size, language support), or side effects. Only basic functionality is stated.

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 concise, with a clear front-loaded sentence explaining purpose, followed by parameter descriptions. Every sentence adds value without redundancy.

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 simplicity and the presence of an output schema, the description covers core aspects. However, it lacks details on input constraints (e.g., file size limits, supported languages) and error conditions, leaving some 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 coverage is 0%, but the description explains both parameters: image as local path or URL, prompt as OCR hint with default behavior. This adds meaning beyond the schema, though more detail (e.g., supported image formats) could be beneficial.

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 images or PDFs (OCR), and provides use cases like screenshots, documents, and code. This makes the purpose unambiguous and distinct from siblings like analyze_image and ocr_precise.

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 cases ('suitable for recognizing screenshots, documents, code text'), but does not explicitly state when to use this tool versus alternatives like ocr_precise. There is no mention of when not to use it.

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