prompt_variants

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

No annotations are provided, so the description carries the burden. It discloses a key behavioral trait ('local, no LLM call') indicating no external call, but it doesn't describe side effects, output format, or determinism. For a transformation tool this is acceptable but incomplete, as it could mention whether the input is mutated or what exactly the output looks like.

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 entire description is a single, front-loaded sentence that gets straight to the point. There is zero waste, with all words contributing to the meaning.

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?

This is a simple utility tool with two parameters and no output schema. The description effectively covers the input (goal+notes), the operation (split), and the output (prompt variants for Claude/GPT/Grok). It could be more complete by specifying the number of variants or the exact format, but for a tool of this complexity, the essentials are covered.

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 0%, so the description must compensate. It mentions 'goal+notes blob' which implies the two parameters are combined into a single blob, giving some semantics beyond the bare schema. However, it doesn't elaborate on the individual meanings of 'goal' and 'notes' or clarify their role in the split, leaving partial clarity.

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 ('Split') and clearly identifies the resource ('goal+notes blob') and the output ('Claude / GPT / Grok oriented prompt variants'). It distinguishes itself from sibling tools by naming the specific models and noting it's local with no LLM call.

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 provides clear context: it's for splitting a goal+notes blob into model-specific prompt variants, with the key qualifier 'local, no LLM call' indicating when to use it (when you don't want an LLM call). However, it doesn't explicitly state alternatives or when not to use it, so it stops short of full exclusion guidance.

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