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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds real value beyond that: it discloses the normalization rules (accents stripped, spaces replaced), the fact that both the original text and the slug are returned, and the 1-credit cost. It does not say how non-Latin scripts or emoji are handled, which is the only notable gap.

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?

Two front-loaded sentences plus a short cost note, with no filler. The 'Slugify: Slugify' opening redundantly restates the name and could be trimmed, but the rest is efficient.

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 three-parameter string transform with a full output schema, this covers input expectations, normalization behavior, and cost. Only exotic edge cases (non-ASCII beyond accents, empty input) are left unaddressed.

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 coverage is 100%, so all three parameters are already documented in the schema. The description reinforces separator and lowercase behavior but adds no formatting rules or edge-case semantics beyond what the schema states, so the baseline of 3 applies.

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 gives a specific verb and resource: converts text into URL-friendly slugs, and names the tunable aspects (separator, lowercase). It is immediately distinguishable from near-neighbors like URLEncoderDecoder or TitleCaseConverter, though it never names those siblings explicitly.

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?

"Pass any raw string" implies the input context, and the URL-friendly framing hints at when this is the right tool, but there is no explicit when-to-use or when-not-to-use guidance and no mention of alternatives such as URLEncoderDecoder or RemovePunctuation.

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