anjian_validate_observation

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

No annotations are provided, so the description carries the transparency burden. It does disclose that human final review is still required before delivery, which is useful behavioral context for a validation/mutation-type tool. However, it doesn't disclose what happens to the observation after validation, whether the decision is reversible, or what side effects recording a decision has on the assessment.

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 a single efficient sentence that conveys key constraints (must explain rationale, human review still needed). It's short and front-loaded with the core purpose. It could add sibling differentiation, but the length is appropriate.

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?

This is a validation tool with 8 parameters, no output schema, no annotations, and 0% schema coverage. The description is far too thin for the complexity of the operation. It doesn't clarify the decision enum semantics, what validation_rationale should contain for each decision type, how severity/recommendation enrich the record, or what happens after recording. A 4-param required tool with an enum deserves substantially more guidance.

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%, so the description must compensate for the 8 parameters, but it only mentions the rationale content requirement. The description doesn't explain what decision values mean (confirmed vs false_positive vs accepted) or the semantics of assessment_id/observation_id beyond the obvious, or how severity/recommendation relate to the decision. With low schema coverage, this is a significant gap.

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 states the tool records manual assisted review decisions, but it's generic and doesn't clearly differentiate from siblings like anjian_consolidate or anjian_run_assessment. The verb '记录' (record) plus the resource '人工辅助复核决定' (manual review decision) gives a reasonable purpose, but the scope/relationship to other anjian tools like anjian_quick_assess or anjian_consolidate isn't clarified.

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 says validation must explain reproduction, applicable conditions, or false-positive reasons, and that human final review is still needed before delivery. This provides some context but no explicit when-to-use or when-not-to-use guidance, nor does it distinguish when to use this versus sibling tools such as anjian_run_assessment or anjian_consolidate.

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