Create POP growth preset (dendritic / coral / lichen)

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

It goes far beyond the annotations by disclosing that POPs are Experimental, par writes are fail-forward, the result reports unverified op/par sets, and warning conditions for divergence (feedback_gain × (1 − decay) ≥ 1.0). These are critical behavioral traits that help an agent anticipate side effects and failure modes.

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 a single dense paragraph, but every sentence contributes useful information and it is appropriately sized for a complex build tool. Minor structural improvements (e.g., separating the output chain from warnings) would elevate it, but it remains focused and front-loaded with the core purpose.

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?

Despite 13 parameters and no output schema, the description is exceptionally complete. It explains the internal node chain (particle_pop, noise_pop, force_pop, feedback_pop), the output path (poptoSOP → geometryCOMP → renderTOP), delegation to buildPopChainScript, and risk warnings. This gives an agent everything needed to understand what the tool does, how it works, and what could go wrong.

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 schema already covers 100% of parameters with descriptions, so the baseline is 3. The tool description adds meaningful system-level context (e.g., how feedback_gain and decay interact to cause divergence, max_points passed defensively, overrides on presets). This enhances understanding of parameter relationships beyond the isolated field 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 opens with a specific verb+resource: 'Build a POP-native reaction-diffusion / growth system inside a fresh baseCOMP.' It clearly distinguishes itself from sibling tools like create_reaction_diffusion or create_pop_field by emphasizing the POP chain, presets, and output chain. The three mode presets further clarify the tool's specific niche.

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 clear context on when to use this tool (for organic POP-based growth systems) and even sets expectations about experimental behavior and divergence risks. However, it does not explicitly mention alternatives or exclusions, so it stops short of a perfect score.

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