KIROK_smart_retain

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

No annotations are provided, so the description must carry the full burden. It discloses the scoring mechanism (LLM, 1-10) and threshold behavior. However, it does not mention cost/rate limits of LLM calls, what happens if the content is below threshold (presumably not retained but not explicitly stated), or any return value structure. Additionally, it does not state that this is a write operation with potential side effects.

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 and well-structured: a one-sentence purpose, a one-sentence usage guideline, then a neatly formatted Args list. Every sentence serves a purpose, with no fluff. It is front-loaded with the most critical information.

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 5 parameters and existence of an output schema (though not shown), the description covers the core logic, usage context, and all arguments. However, it omits details about output/return value, potential prerequisites (e.g., bank existence), and edge cases like invalid timestamps. Still, it provides sufficient guidance for an AI agent to use the tool effectively.

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, so the description's parameter list adds significant meaning: each param is briefly explained (e.g., 'bank_id: Memory bank identifier', 'content: The information to potentially remember'). Default values are noted. This compensates for the bare schema and provides clear semantics.

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 the tool's purpose: evaluating content importance via LLM scoring and retaining only if above a threshold. It distinguishes this from the sibling 'KIROK_retain' tool, which presumably retains without scoring, making the differentiation explicit.

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 explicitly guides when to use: 'Use this for bulk/automatic ingestion where you want the system to decide what's worth remembering.' This implies it's for high-volume or unsupervised scenarios, contrasting with simpler retain or other tools. The guidance is clear and actionable.

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