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

The description adds substantial behavioral detail beyond the readOnlyHint annotation: it explains the verdict schema, cultural grounding via archive matching, the embedded rendered palette with download formats, and the caution not to present archive anchors as recommendations. This gives the agent a clear picture of what the tool returns and how to behave.

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 long but every sentence carries functional weight: core evaluation behavior, output contents, cultural grounding, rendering, download links, and the critical instruction to avoid presenting archive anchors. It is front-loaded with the verdict and returns. A small amount of redundancy exists, but it remains efficient for a tool with this much behavioral nuance.

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 tool with four parameters, a rich output schema, and a complex behavioral contract, the description is complete. It covers input constraints, output components, cultural grounding, downloadable assets, and the follow-up action (palette_finalize). An agent has enough context to invoke the tool correctly and interpret its result.

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 the baseline is 3. The description adds value by specifying the 2-8 hex value bound, providing realistic example values for use_case, market, and medium, and clarifying the relationship between palette, use case, and market. This enriches the schema definitions rather than merely repeating them.

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: evaluate a palette of 2-8 hex values for a use case, market, and medium, and return a verdict with score, colour roles, weakness, and suggestion. It also names the distinct follow-up tool (palette_finalize), which helps separate this evaluation tool from a finalization tool.

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 clearly establishes when to use the tool: when an existing palette needs evaluation against a use case, market, and medium. It also tells the agent to call palette_finalize when choosing its own final palette, which is useful routing. However, it does not explicitly name or exclude sibling alternatives like colour_verdict or palette_analyse.

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