获取用户认知摘要

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds useful behavioral details beyond the annotations, such as the two output formats (system_prompt vs markdown) and the return shape including summary and memory_count. There is no contradiction with the annotations.

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 compact and well-organized: a one-sentence summary of purpose and content, a one-sentence usage recommendation, and a brief Args/Returns breakdown. Every sentence adds value and the most important information is front-loaded.

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 there is only one optional parameter, rich annotations, and no output schema, the description is complete enough. It explains the return value explicitly and describes both possible format values, so an agent has everything needed to invoke it correctly.

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 100% description coverage for the only parameter, including an enum, a default value, and a clear explanation of each format option. The description repeats this information but does not add meaning beyond what the schema already provides, so it earns the baseline score.

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: it returns a compressed user cognitive profile ("印象") with specific content categories like interaction preferences, core profile, domain cognition, known facts, and follow-ups. It distinguishes itself from item-by-item retrieval by explicitly claiming to be more efficient, making it easy to separate from siblings like cognitive_search and cognitive_list.

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 explicitly says when to use it: at the start of a conversation or when a comprehensive understanding of the user is needed. It also frames the tool as more efficient than per-item retrieval. However, it does not name specific alternative sibling tools or state when not to use it, so it stops one step short of full usage guidance.

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