KIROK_reflect

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

Given no annotations, the description clearly explains the process: retrieves memories, analyzes with LLM, saves as mental model. It also details parameters like auto_refresh and source_query for future behavior. No contradictory or missing behavioral traits are apparent.

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 concise with a clear first line stating the purpose, a brief process sentence, and a well-organized Args list. No unnecessary words, though it could be slightly more streamlined.

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?

The description covers the tool's inputs and process adequately. Since an output schema exists but is not provided, it does not need to explain return values. However, it lacks details on error handling or how to retrieve the created mental model, but is largely complete for an AI agent.

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?

With 0% schema description coverage, the description fully explains all 5 parameters, including defaults and max for limit, the purpose of auto_refresh, and the default behavior for source_query. This adds significant meaning beyond the schema's type and default values.

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: 'Reflect on accumulated memories to generate new insights.' It specifies the verb 'Reflect' and the outcome 'generate new insights,' and distinguishes this tool from siblings like KIROK_recall by mentioning it saves insights as 'mental models.'

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 implies usage when one wants to synthesize memories into insights, but does not explicitly state when to use this tool versus alternatives like KIROK_recall or KIROK_consolidate. No 'when not to use' guidance is provided.

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