Create volumetric field

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

The description goes far beyond the sparse annotations by disclosing the full toolchain architecture (noiseTOP → cacheTOP → viewer glslTOP), the approximation nature ('stacked-2D-slice approximation'), performance scaling ('cook cost scales roughly linearly with slice_count × resolution'), and the return format (summary JSON with node errors, warnings, inline preview). These are important behavioral traits that annotations do not cover.

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 dense but every sentence adds value: it covers purpose, architecture, limitation, performance, and return format in a structured flow. It is front-loaded with the core concept and uses no filler. The length is justified by the technical complexity.

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?

With 7 parameters fully documented in the schema, the description focuses on what the schema cannot convey: architecture, performance, limitations, and output. It clearly states the return JSON fields, addresses the main limitation (2D-slice approximation), and gives actionable performance advice. This is comprehensive for a creation tool.

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%, providing a solid baseline of 3. The description adds extra context beyond the schema, such as recommending lower slice counts on integrated GPUs ('drop to 4–8') and explaining the impact of slice_count on rewiring the cache. It also clarifies the turbulence parameter's effect on displacement and noise period, adding practical meaning.

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 starts with a specific verb and resource: 'Build a stacked-slice fake-volumetric noise field'. It lists palettes and explicitly distinguishes itself from a raymarched volume ('NOT a raymarched volume'), which differentiates it from sibling tools like create_volumetric_raymarch. The architecture summary and mention of optional displace+blur further clarify the scope.

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 explicitly says when not to use this tool: 'For a true raymarcher see the planned create_volumetric_raymarch'. It also gives performance guidance ('drop to 4–8 on integrated GPUs'), which helps in choosing appropriate parameters. However, it does not compare against other volume-related siblings like create_sdf_field or create_raymarch_scene, so it lacks a broader when-to-use context.

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