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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds useful behavioral details: it returns percentages, supports stopword filtering, and honors a top N limit. It does not go into edge cases like punctuation handling or case sensitivity, but the added context is meaningful beyond the annotations.

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 compact and front-loaded: the first sentence states the core purpose, the second defines the output, and the third adds filtering behavior and use cases. Every sentence contributes value with no redundancy or filler.

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 tool's simple nature and the presence of an output schema (which explains return structure), the description covers the essentials: purpose, output, stopword behavior, and typical use cases. It does not explicitly address language scope or normalization details, but these are not critical gaps given the schema and tool simplicity.

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%, with each parameter already described (input, top_n, min_length, remove_stopwords). The description reiterates the top N and stopword concepts but does not add new parameter-specific details beyond what the schema provides. 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 states the tool's function with a specific verb ('Analyze') and resource ('word frequency'), and explicitly describes the output ('Returns top N words with counts and percentages'). It distinguishes itself from sibling tools like text_stats and count_tokens by focusing on word-level frequency analysis.

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 clear use cases ('content analysis, keyword extraction, and LLM output analysis') that help an agent decide when to invoke it. However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of full exclusionary guidance.

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