academic_summary

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful context beyond that: results are computed from already-cached data (not a live upstream fetch) and include a stated calculation methodology, plus the caveat that it does not replace official school recognition. It omits whether stale cache affects results, but the added transparency is solid.

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?

One dense, front-loaded sentence that leads with the computation and its inputs, then appends the methodological caveat. Nothing is padded, though the semicolon-separated clauses require careful parsing.

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?

An output schema exists, so return values need not be described, and the description covers the data source and the non-official caveat. The remaining gap is the undocumented semester parameter, which is material for a calculation tool whose behavior changes depending on whether it is set.

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 description coverage is 0% and the single 'semester' parameter (with an empty-string default) is never mentioned in the description. Worse, the phrase '按已缓存的全部成绩' ('all cached grades') suggests an unscoped computation, leaving the agent to guess how semester filtering interacts with the summary.

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?

States a specific action (calculate credit-weighted average score/GPA) on a specific resource (all cached grades), which clearly differentiates it from the raw-data siblings like get_grades and get_exams. It stops short of naming an alternative tool, but the verb+resource pairing is unambiguous.

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?

Implies usage context by noting the result derives from cached grades and is not an official school determination, which tells the agent this is an informational derivative metric. However, it never says when to prefer this over get_grades, nor whether the empty-default semester should be supplied to scope the calculation.

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