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

With no annotations, the description carries the full burden and does well: it discloses cost (5 sats per image), the Bitcoin Lightning pay-per-request model with no API key, endpoint stability, and the quality claims behind the model. It does not cover failure/refund behavior or result-retrieval flow, leaving some behavioral gaps for a paid mutation 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 core purpose and payment prerequisite are front-loaded, and each sentence is functional. Some benchmark/marketing claims (ICCV 2021, PSNR 32.73dB, 100M+ runs) are padding, but they serve as differentiation evidence and do not obscure the call instructions.

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?

For a no-annotation, no-output-schema tool the description covers the main complexity — the payment prerequisite, cost, and model stability — and the resultKey schema field handles retry semantics. It does not state whether results return synchronously or require polling await_result/check_job_status, leaving one meaningful gap.

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 every parameter including the scale enum and face_enhance is already documented in the schema. The description echoes scale, face enhancement, and the paymentId prerequisite but adds no syntax or format detail beyond the structured fields, which matches the baseline 3 for fully documented schemas.

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 ('Upscale images 2x or 4x with neural super-resolution') and immediately distinguishes the operation from mere interpolation and from sibling image tools like deblur_image or restore_face by naming the Real-ESRGAN approach. An agent can identify the correct tool without inspecting the schema.

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?

It clearly states the prerequisite workflow ('Requires create_payment with toolName="upscale_image"') and the payment cost, which tells the agent exactly what must precede a call. It stops short of explicitly naming alternatives (e.g., edit_image or restore_face for faces only) or when-not-to-use conditions, so it is clear on context but lacks exclusions.

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