word_freq

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

The description discloses that it is case-insensitive and uses a 'simple tokenizer', which provides some insight into its behavior. However, with no annotations provided, it doesn't mention whether it's read-only, what the return format looks like, or how punctuation and stop words are handled. This leaves significant behavioral aspects undisclosed.

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 concise sentence that front-loads the core function. Every word contributes meaning, with no 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?

The tool has no output schema, and the description does not specify the return format, ordering of frequencies, or whether a list of (word, count) pairs is produced. Given its simplicity, some information is still missing for a caller to fully understand the 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?

The schema documents the 'limit' parameter with a default value, and the description implies that 'text' is the input string, adding minimal context. Since the description does not explicitly describe the 'text' parameter's format or constraints, it only partially compensates for the low schema coverage.

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 the tool computes top word frequencies from text, naming the resource (text) and the nature of analysis. It distinguishes from sibling text tools by focusing on word frequency rather than formatting or conversion. However, the verb is implicit, so it's not a perfect 5.

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 like diff_text or reading_time. It doesn't mention any exclusions or specific use cases. The context is only implied by the phrase 'word frequencies in text'.

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