Suggest ways to split a story

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

With no annotations, the description carries the behavioral burden. It usefully says each suggestion includes how to apply the pattern and an example, which clarifies the return content, but it does not explicitly confirm that the operation is read-only or describe side effects/auth needs.

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 a single efficient sentence that front-loads the triggering condition and then clearly lists the pattern categories and output shape. Every part earns its place and there is no filler.

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 non-mutating suggestion tool with a fully documented two-parameter schema and no output schema, the description is complete enough for an agent to invoke it correctly. It explains the input precondition, the pattern catalog, and what each returned suggestion contains.

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?

The input schema provides 100% description coverage for both parameters, including the meaning and default for maxSuggestions. The description does not add parameter-level guidance beyond the schema, so the baseline score of 3 is appropriate.

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 uses a specific verb ('suggests splitting patterns') and resource ('story or feature that is too big'), and lists the exact kinds of patterns returned. This clearly distinguishes it from the sibling validation/export tools without needing to name them 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?

It gives a clear usage condition: use this tool when a story or feature is too big and needs splitting. It does not explicitly state when not to use it or name alternatives, but the context is strong enough for correct selection.

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