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

With no annotations, the description carries the full behavioral burden and does well: it discloses cost (110 sats per image), latency (~2 min processing), payment mechanism (Bitcoin Lightning, no API key/signup), and the required create_payment step. It stops short of describing the return format or failure behavior, which matters for an image-processing tool.

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 paragraph is compact and front-loaded: purpose first, then model provenance, best-for/not-for, supported modes, and finally cost/payment details. Every sentence contributes operational value.

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 four parameters with full schema coverage, no output schema, and no annotations, the description covers purpose, exclusions, cost, latency, and payment prerequisites thoroughly. It omits the return value shape and how results are delivered, which would complete the picture for a paid image-processing call.

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 all four parameters are already documented in the input schema. The description adds useful context that denoising is supported, but no syntax or format detail beyond what the schema provides, so the 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 states a specific verb and resource ('Recover detail from camera-shake and accidental motion blur') and names the underlying model (NAFNet). It clearly scopes what the tool is and is not for, though it never names a sibling tool to route against, leaving some ambiguity with upscale_image or restore_face.

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?

Explicit 'Best for' and 'NOT effective for' lists tell the agent exactly when to select and avoid this tool. It also explains the payment precondition via create_payment, leaving no inference needed for correct invocation.

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