用户记忆统计

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

Annotations already cover read-only, idempotent, and non-destructive behavior, lowering the burden on the description. The description adds value by naming the exact categories of statistics returned, giving concrete behavioral context about the tool's output without contradicting any annotation.

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 one tight sentence that leads with the action verb, lists the concrete statistics, and closes with the intended purpose. Every clause contributes information; there is no redundancy or 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?

With no output schema, the description rightly carries the burden of explaining the return payload and does so by listing the main statistical categories. It remains slightly incomplete because it does not specify the exact format (counts vs. percentages) or the structural shape of the response, and it does not explicitly distinguish itself from cognitive_summary.

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?

There are zero parameters and schema description coverage is vacuously 100%, so the baseline is 4. The description compensates by detailing what the no-argument call returns—total, type distribution, domain distribution, and confirmation-rule count—making the empty input schema meaningful.

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 specifies a verb ('返回', returns) and a resource ('记忆存储的统计信息'), then enumerates the exact metrics: total, distribution by type, distribution by domain, and confirmed-rule count. This makes it easily distinguishable from sibling tools like cognitive_search and cognitive_write.

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 gives a clear usage context—'用于了解记忆覆盖面' (for understanding memory coverage)—which tells an agent when a stats overview is appropriate. However, it does not explicitly contrast with cognitive_summary or cognitive_list, so an agent might still be unsure which aggregate view to pick among those siblings.

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