get_learner_brief

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

No annotations are present, so the description carries the full burden—and it excels. It discloses default/clamping behavior for limit, active-pair fallback for pair_id, redaction of forbidden hypotheses, etag caching semantics, error behavior with available pairs, and lesson_id preference with fallback. It even details the semantics of confidence_facts (pure numeric, no adjectives/percentages).

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 long but extremely dense and well-structured with labeled return sections, a 'red line' redaction note, an etag contract, and fallback logic. Each sentence adds distinct information; there is no filler or repetition. The upfront '开课前先调这个' is a high-signal opening.

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?

There is no output schema, so the description fully compensates by enumerating every return field with precise semantics, including edge cases (null confidence_facts, redacted hypotheses, lesson_id no-match fallback), error behavior, and caching. For a 3-parameter tool with no annotations and no output schema, this description is complete and self-sufficient.

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?

Schema coverage is 67% (pair_id lacks description), but the description compensates fully by explaining pair_id's default (current active pair), limit's default and clamp, and lesson_id's filtering/fallback semantics. It adds behavioral meaning far beyond the schema, including which parameters are optional and their interaction with the return payload.

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 uses a specific verb-resource pairing ('读回学生模型'), states when to invoke ('开课前先调这个'), and enumerates exactly what is returned. It clearly distinguishes this from generic getters by scoping to learner_hypotheses / post_lesson_evaluations / teacher_reflections and explicitly says not to manually query tables.

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?

Explicitly states '开课前先调这个' as the primary usage context and instructs the agent to avoid manual table reads ('不用自己扒表'). It also gives conditional guidance for optional parameters (like lesson_id fallback behavior). It does not name alternative sibling tools for exclusion, but the context is strong enough to guide selection.

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