nebula_color

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the output quantities, between-observer comparisons, and scotopic/photopic ratio, but it does not describe return structure, side effects, failure modes, or how the output varies with input 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?

It is a single front-loaded sentence that immediately states the core computation and then enumerates outputs. It is dense but efficient; the only minor weakness is that the long metric list is less scannable than a short structured summary.

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?

The tool has 10 parameters, no annotations, and no output schema, so the description must carry more context than usual. It lists the output metrics well, but does not explain the returned structure or the relationships among templates, custom lines, continua, and reddening options, leaving the agent to rely on the schema for call construction.

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 description coverage is 90%, so the input schema already defines nearly all parameters in detail. The description adds no parameter-level meaning beyond that, so the baseline of 3 is appropriate.

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 identifies a colorimetric computation for nebular spectra and lists the specific quantities produced. It implicitly distinguishes itself from plotting siblings by focusing on numeric colour metrics, but it never names or explicitly contrasts with alternatives such as render_color_swatches or plot_chromaticity_comparison.

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 explains what the tool computes but gives no explicit guidance on when to choose it over sibling tools. There are no stated exclusions, prerequisites, or alternative-tool routing despite several related plotting and colour rendering siblings.

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