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

Beyond the readOnly/idempotent/openWorld annotations, it discloses real behavior: private addresses are refused, the decoded file is capped at 10 MB, non-default language packs are fetched lazily on first use, results carry a 0-100 confidence, and a textless image yields an empty string. These are concrete operational traits an agent could not infer.

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?

Six short, front-loaded sentences with no filler; purpose, inputs, constraints, and return shape each get exactly one sentence.

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?

With an output schema covering return values and annotations covering the safety profile, the description fills the remaining gaps an agent needs: input formats, SSRF guard, size cap, language behavior, and empty-result handling.

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 url/data/filename/mimeType and the eng default are already documented. The description mostly restates these; only the lazy-fetch note on additional language codes adds meaning beyond the schema, so the baseline 3 holds.

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?

States a precise verb+resource+engine: extract text from an image via Tesseract, which clearly separates it from optical siblings like read_barcode. It never names an alternative explicitly, so sibling differentiation is only implicit.

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?

It tells the agent the accepted input forms (base64 or public URL) and the default language, which helps invocation. But it never says when to choose this over read_barcode, extract_pdf_text, or convert_image, so usage is implied rather than stated.

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