duval_factorize

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

No annotations are provided, so the description carries the full disclosure burden. It implies a pure, side-effect-free computation by naming a deterministic algorithm, which is reassuring, but it does not state that it is read-only or describe the return structure (the list of Lyndon factors) or performance characteristics.

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?

A single front-loaded sentence with zero filler; every word (verb, resource, algorithm, input type) earns its place.

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 compute tool with no output schema, the description should describe what is returned (the ordered sequence of Lyndon factors), which it omits. Combined with the absence of annotations, there is a gap, though the input side is fully covered by the schema.

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%: the single 'text' parameter is fully documented, including the 1,000,000-char limit. The description adds no syntax or format detail beyond 'a string', so the schema does all the work and baseline 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?

States a specific verb ('Compute'), a precise mathematical resource ('the Lyndon factorization of a string'), and even names the underlying method (Duval's algorithm). This is far more specific than a tautology and lets an agent distinguish it from other string algorithms, though it does not explicitly contrast itself with siblings like suffix_array_build or z_algorithm.

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 gives no when-to-use context, no prerequisites, and no alternatives. An agent learns what it computes but nothing about when this operation is preferable to other string-decomposition tools.

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