word_count

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

No annotations are provided, so the description carries the full burden. It states the core behavior clearly, but it does not disclose how words are tokenized or whether punctuation, newlines, or empty strings affect the result. There is no contradiction with any structured metadata.

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 sentence with no wasted words. It front-loads the action and is easy to parse quickly.

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?

This is a very simple tool with one parameter, and an output schema is present, so return-value documentation is not required. The only notable gap is the lack of word-boundary details, which could matter for edge cases, but the description is otherwise complete enough for correct invocation.

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 0%, and the description does not explicitly document the text parameter. However, with a single required string parameter named 'text' and a tool that counts words, the parameter's purpose is strongly implied. It adds minimal meaning beyond the schema but is sufficient for this simple case.

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: 'Count the number of words.' It clearly distinguishes word_count from siblings like uppercase, reverse_text, and arithmetic tools, even though it does not explicitly name alternatives or scope the input.

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 gives no guidance on when to use this tool versus alternatives, nor does it mention any preprocessing expectations like whitespace normalization. However, the name and siblings make its general role fairly obvious.

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