filter_profanity

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains censoring behavior (replacement with asterisks), notes the use of a curated English profanity list, and lists the returned keys. It omits minor details like exact asterisk formatting, but the core behavior is transparent.

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 well-structured and front-loaded with the core purpose. The behavioral note and Args/Returns sections are concise and every sentence adds value without 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 is simple and the description covers all essential aspects: input, behavior, and return keys. Since there is no output schema, listing the return keys is especially valuable. A minor note on the exact censoring format could be added, but nothing critical is missing.

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 only provides a title for 'text' with no description, so the 'Args' section adds necessary meaning and an example. The single parameter is clearly explained, compensating for the 0% schema description 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 clearly states a specific function: detecting and censoring profanity in text. It names the resource ('profanity in text') and the actions ('detect and censor'), which makes its purpose unmistakable and distinguishes it from the unrelated sibling tools.

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 implies when to use the tool: whenever English profanity must be flagged or censored in text. However, it does not explicitly contrast it with alternatives such as classify_text, nor does it state when not to use it.

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