generate_slug

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 concretely explains normalization, punctuation removal, whitespace collapsing, lowercasing, and the separator options, and it discloses the return dict keys. It does not mention edge cases like empty input or max_length truncation semantics, but the core behavior is well covered.

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 front-loaded with the purpose, followed by compact transformation details and a structured Args/Returns section. Every sentence adds information, and the example is illustrative without being padded.

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?

For a low-complexity utility with no output schema, the description is complete enough: it states inputs, transformations, parameter semantics, and the return format. The agent can confidently call the tool with correct arguments and know what the response will contain, including the cost field.

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. It adds useful meaning beyond the schema: a concrete text example, allowed separator values ('-', '_', '.'), and clarification that max_length is optional. It does not fully specify how max_length truncates the slug, but each parameter is meaningfully described.

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 opens with a specific verb and resource: 'Generate a URL-safe slug from any text.' It further distinguishes the tool by listing exact transformation behaviors (Unicode normalization, punctuation removal, whitespace collapsing, lowercasing), leaving no ambiguity about what it does.

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 phrase 'from any text' gives a clear and general usage context, and the example input 'Hello World! Café & More' demonstrates representative inputs. It does not explicitly name alternatives or exclusions, but none of the sibling tools perform slugification, so this is a minor gap.

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