vision_extract_text

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

No annotations provided, so description carries full burden. Discloses use of dedicated OCR model with automatic fallback to VLM, and mentions output is structured by reading order. Lacks details on authentication, rate limits, or behavior with empty images.

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 sentences, each with a distinct purpose: function definition, use cases, model behavior. No filler, front-loaded with the core action. Conciseness earned.

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?

Despite 5 parameters and 0% schema coverage, description omits parameter details and output specifics beyond 'structured text'. Output schema exists but not leveraged. Tool complexity warrants more explanation 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 description coverage is 0% and description does not explain any of the 5 parameters (language, include_raw, image_source, etc.). Adds no meaning beyond the schema itself, which also lacks descriptions. Baseline expectation: description should compensate.

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?

Clearly states it extracts text from images via OCR and returns structured text by reading order. Lists specific use cases (screenshots, documents, receipts, etc.) and distinguishes from sibling tools like vision_analyze which analyze images rather than extract text.

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 recommends using for text-heavy images with examples. Does not specify when not to use or provide alternative tools, but the context and sibling list imply guidance. Lacks exclusion criteria like 'if no text present'.

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