check_profanity

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

With no annotations and no output schema, the description carries the full burden of disclosing behavior. It lacks details on whether the tool returns a censored string, a boolean, or both, and does not mention language support, error handling, or side effects. This is a significant gap for a function.

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, front-loaded sentence with no redundancy or unnecessary words. Every word contributes to the purpose.

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 (one parameter, no output schema), but the description still leaves essential unknowns: what the return value looks like, whether it modifies input, and how it handles edge cases. The description is too sparse for an agent to use it confidently without further explanation.

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%, so the description must compensate. However, it only says 'text' without adding any additional meaning about format, encoding, length limits, or expected content. The parameter name and type in the schema already convey that it takes a string of text.

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 uses specific verbs 'detect' and 'censor' with resource 'profanity in text', clearly stating what the tool does. It is distinct from all sibling tools (e.g., translate_text, analyze_sentiment, validate_email), leaving no ambiguity about its function.

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 usage for filtering or checking profanity but provides no explicit guidance on when to prefer this tool over alternatives or any exclusions. Since no sibling tool covers profanity, the context is clear but still only implicit.

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