Rendering research register

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds valuable context beyond the annotations: 'zero-auth, no external side effects' and 'Returns JSON research seeds' disclose auth requirements and return format, which the annotations do not provide.

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?

Two tightly written sentences with zero waste. The usage condition is front-loaded, followed immediately by behavioral traits and return format, making the definition easy to scan and act on.

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 zero-parameter, read-only research register with no output schema, the description supplies everything an agent needs: when to use it, auth and side-effect profile, and return format. No additional context is required to invoke it 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?

The tool takes zero parameters, so there are no parameter semantics to document; the baseline for 0 params is 4. The description does not need to add parameter detail, and its mention of return format is appropriately separate.

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: collecting rendering leads and returning JSON research seeds, with domain qualifiers (clustered-forward, Gaussian splatting, creative coding, graphics demos). It clearly distinguishes itself from generic research tools by focusing on rendering, though it does not explicitly differentiate from the closest sibling, telos.rendering.capabilities.

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 opens with an explicit 'Use when collecting rendering leads...' condition, giving clear context for when to invoke the tool. It does not name alternatives or state when not to use it, but the use case is unambiguous.

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