ai_analyze_colors

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

No annotations are provided, so the description carries full burden. It states the tool analyzes colors but doesn't disclose behavioral traits such as what the analysis returns (e.g., color palette, usage frequency), whether it's read-only or has side effects, or any performance considerations. This is a significant gap for a tool with no structured safety hints.

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, efficient sentence in Korean that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes to understanding the 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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the analysis entails or what results to expect (e.g., a list of colors, statistics). For a tool that likely returns data about canvas colors, this omission leaves the agent uncertain about the tool's behavior and output, reducing its effectiveness in context.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied since the schema fully covers the lack of parameters, and the description doesn't need to compensate.

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 states the action ('analyzes') and target ('colors used on the current canvas'), making the purpose understandable. It doesn't explicitly differentiate from sibling tools like 'ai_suggest_colors' or 'style_fill', but the verb 'analyze' implies examination rather than suggestion or application, which provides some implicit distinction.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an active canvas), exclusions, or comparisons to siblings like 'ai_suggest_colors' (which might recommend colors) or 'style_fill' (which applies colors). This leaves the agent with minimal context for tool selection.

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