Slugify

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

Annotations cover the safety profile (readOnly, idempotent, closed-world), and the description adds substantial transformation semantics beyond that: accent folding with concrete examples (é → e, ß → ss), Arabic transliteration vs letter-keeping, Arabic-Indic digit conversion, tashkeel removal, the '&' → 'and' rule, and word-boundary-length capping.

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?

One dense but front-loaded sentence: purpose first, then transformation rules in a scannable order, with the demo URL last. It is long but nearly every clause earns its place; the trailing URL is marginal value for an agent.

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?

With no output schema, the description still implies the return shape through 'one slug per non-empty line (bulk)', and annotations cover safety. For a 7-parameter read-only text transformer it is close to complete, though it could be more explicit about the output payload (array vs newline-joined string).

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 every parameter already carries its own description and defaults; the baseline is 3. The description largely restates what the schema documents (folding accents, stop words, length cap, Arabic mode) rather than adding syntax or interaction detail the schema lacks.

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?

States a specific verb and resource ('Turn titles into clean URL slugs') plus the bulk granularity ('one slug per non-empty line'). It does not name or contrast with similar siblings such as text_case, text_clean, or text_arabic, so an agent must infer the boundary itself.

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 tool's purpose implies its usage (URL slug generation, bulk title lists), and options like stop-word removal and Arabic transliteration hint at applicable contexts. But there is no explicit when-to-use/when-not statement and no routing advice against the neighboring text_normalization tools.

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