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

Annotations already declare readOnlyHint and idempotentHint. The description adds value by specifying input constraints (base64-encoded, max ~15 MB), hinting at OCR for images, and stating return structure. No contradictions with annotations.

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 front-loaded with the core functionality, then usage guidance, then examples. It is concise with minimal redundancy, though the examples could be merged. Still efficient and to the point.

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 output schema exists (returns pages and text), the description doesn't need to explain return values. It covers input format, usage context, alternatives, constraints (size, OCR), and example prompts. Complete for the tool's complexity.

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% with both parameters described. The description provides example base64 strings and MIME types, but these largely duplicate schema examples. It adds usage context but not significantly more semantic clarity. Baseline 3 is appropriate.

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 plain text from PDF or image (base64-encoded), using a specific verb and resource. It distinguishes from sibling url.extract by noting the base64 requirement, making the purpose unambiguous.

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 says 'Use when you need raw text for downstream AI analysis' and provides example prompts. It also tells when not to use it: 'For documents at a public URL, use url.extract instead.' This provides clear guidance on alternatives.

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