extract_text

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

No annotations provided, and the description does not disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or output format. The mention of 'high-quality' is subjective and not actionable.

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 with no redundancy, front-loading the core purpose. Every sentence adds value, making it very concise.

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 a simple tool with output schema and full parameter coverage, the description adequately defines the tool's purpose and use cases. Missing details like output format or limitations are partially compensated by the output schema.

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 100%, so the schema already documents parameters. The description does not elaborate on parameters beyond the schema, but the context 'Google Search grounding' adds minimal value to the boolean parameter.

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 the tool extracts text from images using OCR, specifying the resource (image) and action (extract text). Distinguishes from sibling tools like analyze_image and detect_objects by focusing on text extraction.

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?

Provides examples of supported image types (screenshots, documents, signs, handwriting), helping users understand applicable scenarios. However, lacks explicit when-not-to-use or comparison with alternatives like analyze_image.

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