extract_text

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

With no annotations present, the description carries the full burden of behavioral transparency. It discloses that the tool extracts 'all visible text' and preserves layout 'where possible,' and explains how the language_hint behaves. It does not discuss failure modes, but for a non-destructive OCR tool this is adequate.

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 well-structured with clear sections for usage, arguments, returns, and examples. It is slightly verbose but every sentence provides useful information, earning a strong score without being overly terse.

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 tool with two simple parameters and an output schema present, the description is complete. It covers purpose, ideal use cases, parameter semantics, return format, and provides examples. No critical information is missing.

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 input schema has 0% description coverage, but the 'Args' section fully compensates. It explains that 'image' can be a file path, URL, or base64, and that 'language_hint' is optional with concrete examples. This adds significant semantic meaning beyond the raw 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 'Extract (OCR) all visible text from an image,' which is a specific verb and resource. It distinguishes itself from sibling tools like describe_image and analyze_chart by focusing on text extraction rather than visual appearance.

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 explicitly identifies ideal use cases: 'screenshots of documents, code, error messages, or any image where the text content matters more than the visual appearance.' This provides clear guidance on when to use the tool, though it does not explicitly state 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.