to_snake_case

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

With no annotations, the description carries the full burden, and the bracketed "verified pure command" tag does add real behavioral context: no side effects, deterministic output. It does not disclose edge-case handling (acronyms, punctuation stripping, Unicode) or the return shape, which matters for a transformation tool.

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 short sentences, front-loaded with the operation and then the purity guarantee. The bracketed meta-tag is slightly noisy but small and conveys a genuine property.

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 simple pure single-argument transform with no output schema, the description covers the core action, but it omits how ambiguous inputs are handled and what exactly is returned. Enough to call it, not enough to predict results for mixed-case or punctuated input.

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?

One required parameter with 0% schema description coverage; the description only refers generically to "a string" rather than naming or describing the text input. For a single obvious input this is close to adequate, but it adds no meaning beyond the parameter's name and type.

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 target format ("Convert a string to snake_case"), so the transformation is unambiguous. It does not distinguish itself from the sibling tool slugify, which performs a closely related normalization, leaving an agent to infer the boundary.

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?

There is no statement of when to use this over slugify or normalize_phone, no preconditions, and no exclusion cases. The only guidance is the implied 'if you need snake_case output', which the agent must infer entirely from the name.

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