record_learner_hypothesis

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

With no annotations provided, the description carries the full behavioral burden and does so richly: forbidden-domain writes silently return an explanation without error, rejected/frozen/confirmed sovereignty rules are spelled out, revise leaves expired traces, and stale hypotheses are only hinted at without auto-retirement. This goes far beyond minimal mutation disclosure.

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 dense and front-loaded with the core purpose, and every sentence carries necessary policy or behavioral information. However, it is a single long unbroken paragraph mixing creation rules, prohibitions, lifecycle semantics, and sovereignty; bullet or section structure would improve scanability. Only a minor structural deduction.

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?

Without an output schema or annotations, the description still covers purpose, creation timing, prohibited domains, lifecycle behavior, sovereignty rules, stale status handling, and even the no-write response behavior. It provides enough context for an agent to select and correctly invoke the tool in complex scenarios.

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?

Despite 100% schema coverage, the description adds substantial meaning beyond the schema: pairing hypothesis_id with action switches to lifecycle mode, giving hypothesis_id alone errors, domain triggers a forbidden-registry check, evidence_event_ids append to the existing evidence ledger, and action-specific parameter requirements are clarified.

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 opens with '写一条 LearnerHypothesis' (write a LearnerHypothesis), specifying the exact verb and resource, then immediately describes lifecycle variants (create/reinforce/revise/retire). It explicitly distinguishes itself from reflect_on_teaching by stating that confidence-level judgments belong to that tool, not this one.

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?

It gives explicit when-to-use rules ('3 课之后才允许首次写'), when-not-to-use rules (domain hits forbidden registry, confidence judgments), and names the alternative tool (reflect_on_teaching). It also provides decision guidance for lifecycle actions: '有证据就 reinforce, 被推翻就 revise/retire'.

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