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

Annotations already cover readOnlyHint and openWorldHint, so the safety profile is handled. The description adds genuinely useful context beyond structured fields: the $0.009 USDC per-call cost via x402, which affects invocation decisions.

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 tight sentences, front-loaded with the capability, then inputs, use cases and cost. No redundancy or filler; every sentence carries information an agent needs.

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 no output schema, the description adequately conveys that the return includes both a description and extracted text. For a zero-required-parameter tool this is nearly complete, though it could hint at output structure or the 8 MB limit already in 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 description coverage is 100%, so url, prompt and image_base64 are already documented in the schema. The description restates url/base64 as alternatives and prompt as optional, adding no format or syntax detail beyond that, so baseline 3 applies.

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 specific verb and resource ('Describe an image and read every piece of visible text'), and the '(vision LLM)' tag clarifies the mechanism. It does not differentiate from siblings like ocr or vision_ask, which overlap heavily with the text-extraction claim.

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?

Offers concrete use cases ('receipts, screenshots, signs and charts') and input options, which implies when to use it. However it names no alternatives and gives no when-not guidance, so an agent cannot tell when to prefer ocr or vision_ask instead.

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