deblur_image

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

With no annotations, the description carries full burden. It discloses cost (20 sats), processing time (~2 min), payment method (Bitcoin Lightning), and prerequisite (create_payment). Does not mention file size limits or invalid image handling, but overall thorough.

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 concise (3-4 sentences), front-loaded with the main purpose, and each sentence provides necessary context without redundancy. Highly efficient.

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 3 parameters, no output schema, and a payment workflow, the description covers the core usage, cost, timing, and dependency on create_payment. Missing output format details, but acceptable for a processing tool with no structured output.

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 baseline is 3. The description adds value by explaining paymentId must be paid, specifying task_type defaults ('Image Debluring (GoPro)'), and clarifying the three options. This goes beyond the schema's enum descriptions.

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, names the model (NAFNet), and lists both effective and ineffective scenarios, distinguishing it from sibling tools like 'upscale_image' or 'edit_image'.

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?

Explicitly states when to use (handheld shake, bumped camera, whole-frame uniform blur) and when not to use (intentional panning, bokeh, artistic motion). Also mentions support for denoising and payment requirements, providing clear alternatives and prerequisites.

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