generate_3d_model

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

With no annotations, the description carries the full burden. It discloses key behaviors: async operation (returns requestId), cost per model, payment requirement with Bitcoin Lightning (no API key needed), and the use of Seed3D technology. It does not cover error handling or rate limits, but the main behavioral traits are well specified.

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 (4 sentences) and front-loaded with the main action. Every sentence adds distinct information: purpose, technology, async nature, cost, payment method, and prerequisite. No wasted words, efficient and clear.

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 the tool's complexity (3 params, async, payment flow) and no output schema, the description covers the primary use case, async polling, cost, and payment prerequisite. It sets expectations for output (GLB model, requestId) but omits error handling details. Overall, it is sufficiently complete for an agent to use correctly.

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 beyond schema: it clarifies that paymentId must be a paid valid ID (referencing create_payment), that imageBase64 should be a single photo in valid formats, and that modelId can be omitted for default. This enriches parameter understanding.

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's purpose: 'Convert a single photo into a textured 3D GLB model.' It uses a specific verb (convert) and resource (photo to 3D model), and distinguishes from siblings by noting async behavior, cost, and payment requirement, which are unique among sibling tools.

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?

The description provides explicit context: when to use (to generate a 3D model from a photo), async nature (poll with check_job_status), cost (350 sats), and payment method (requires create_payment). It does not explicitly state when not to use or list alternatives, but given no similar sibling tools, the guidance is clear and actionable.

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