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

With no annotations, the description carries the full load and mostly succeeds: it discloses cost (25 sats/image), the payment model (Lightning, no signup/API key), that background enhancement and face upsampling happen by default, and that the model is upgraded server-side under a stable endpoint. It omits what is returned on success and what happens on failure or timeout, which is a notable gap for a paid, non-deterministic-generative call.

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 purpose is correctly front-loaded in sentence one, but several later sentences are marketing rather than decision-relevant ('NeurIPS 2022, state-of-the-art FID 32.65 on CelebA-Test', 'Stable endpoint — model upgrades automatically as SOTA evolves'). The benchmark score and venue do not help an agent decide or invoke.

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?

Seven parameters, two required, no output schema and no annotations means the description must be thorough. Payment prerequisites and cost are well covered, but the return value is never described and the resultKey idempotent-retry mechanism appears only in the schema, leaving the agent unprepared for async behavior.

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 the baseline is 3. The description's fidelity sentence and 'also enhances background and upsamples' largely restate what the schema already documents for fidelity, background_enhance, and face_upsample, and it adds no practical guidance on choosing a fidelity value or a re-call strategy with resultKey.

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 ('Restore ... faces') plus the exact failure modes it targets (blurry, damaged, AI-generated), so the agent immediately knows what capability this is. It never mentions the near-duplicate siblings deblur_image or upscale_image, so the agent must infer the boundary itself.

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 gives a genuinely useful prerequisite: 'Requires create_payment with toolName="restore_face"', which tells the agent this cannot be called cold. But there is no when-to-use-vs-alternative guidance against deblur_image (non-face blur) or upscale_image (resolution only), which are the most confusable siblings in a 50+ tool catalog.

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