KIROK_list_mental_models

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

No annotations are provided, so the description must carry the transparency burden. It implies read-only behavior by describing the action as 'list' and mentions a default limit. However, it does not disclose side effects (none expected), authentication needs, pagination behavior, or ordering. The transparency is adequate but not comprehensive.

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 extremely concise: two sentences plus an Args block. Every word adds value, with the purpose front-loaded ('List mental models') and parameter details compactly listed. No redundancy or filler. It's an model of efficiency.

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?

Given that an output schema exists (thus return values are documented elsewhere), the description covers the essential context: what the tool does, its required input (bank_id), and its optional parameter (limit). It does not mention pagination or ordering, which could be relevant for a list operation. Still, for a simple list with output schema, it is fairly complete.

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 has 0% description coverage, but the description provides clear semantics for both parameters: 'Memory bank to list mental models from' for bank_id and 'Maximum number of models to return (default 10)' for limit. This adds meaning beyond the raw schema, which lacks descriptions. The parameter explanations are accurate and helpful.

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 clearly states 'list mental models' and specifies they are 'insights generated by Reflect'. It identifies the resource (mental models) and action (list), distinguishing it from siblings like KIROK_get_mental_model (single) and KIROK_refresh_mental_model (update). The resource scope is well-defined.

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 indicates the tool is used 'for a bank' and requires a bank_id, providing context for when to use it. However, it does not explicitly state when not to use it or suggest alternatives (e.g., use KIROK_get_mental_model for a specific model). The usage context is clear but lacks exclusionary guidance.

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