Word Cloud

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's additional context about word sizing adds some value but does not contradict annotations. Behavioral traits beyond safety are not detailed.

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 two sentences and fairly concise, but the first sentence is a quoted phrase that adds little structure. It could be more efficiently worded without losing information.

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 high schema coverage and no output schema, the description covers the core functionality and use cases. However, it does not mention output format or behavior for edge cases, leaving some gaps for a rendering 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 description coverage is 86%, so baseline is 3. The description references the 'value' parameter (frequency/importance) but does not explain other parameters like theme, effects, or palette. It adds marginal meaning beyond the schema.

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 it renders a word cloud and explains the purpose: identifying dominant themes with words sized by frequency or importance. It distinguishes itself from 36 sibling render_* tools by specifying the chart type and use cases.

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?

It provides explicit use case examples (survey responses, keyword analysis, topic frequency) but does not mention when not to use it or alternatives among sibling tools. Some guidance is given, but exclusions are absent.

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