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

With no annotations, the description fully discloses behavior: processing time (~2 min), cost (20 sats), payment via Lightning, model details, and requirements (payment from create_payment). This adequately informs the agent of all relevant traits.

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 somewhat lengthy but every sentence adds unique value, covering purpose, limitations, alternatives, pricing, and payment flow. It is front-loaded with the core functionality, though minor trimming could improve clarity.

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?

The description provides comprehensive context given no output schema: it explains processing time, cost, payment flow, model performance, and limitations. It lacks explicit return format but the tool's output can be inferred (a deblurred image).

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?

All three parameters are described in the schema (100% coverage). The description adds value by explaining the difference between option enum values, default behavior, and the paymentId requirement, enhancing understanding beyond the schema.

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 recovers detail from camera-shake and accidental motion blur, specifies the model and benchmarks, and explicitly lists what it is not effective for, making it highly specific and distinct.

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 when-to-use and when-not-to-use conditions are provided, including best uses (handheld shake, bumped camera) and ineffective cases (panning blur, bokeh). Also mentions denoising support and payment requirements, guiding appropriate invocation.

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