save_method_review

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

Annotations already declare this is a write (readOnlyHint=false) that is non-destructive, so the safety profile is covered. The description usefully adds that the stored content is an AI-generated evaluation rather than a verified fact, which is meaningful semantic context, but it says nothing about overwrite behavior, auth requirements, or what happens on repeat saves.

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?

A single front-loaded sentence with a semicolon separating the action from the caveat; no filler. It is appropriately compact, though the caveat could be tightened into the primary clause.

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?

For a five-required-parameter write tool with 0% schema coverage and no output schema, the description is too thin: it never defines the parameter contract (required evidence linkage is only mentioned in prose) nor the response behavior. The agent knows the semantic intent but not how to populate the fields 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?

Schema description coverage is 0% across 5 required parameters, so the schema provides no per-parameter help. The description only loosely gestures at 'rationale' and 'limitations' concept, leaving work_id, evidence_ids, and research_type completely unexplained in both places. It does not compensate for the coverage 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?

States a specific verb (保存/save) and resource (方法评价及局限/method review and limitations), and adds scope constraints (must link to the source document; it is an AI evaluation, not a confirmed identity or quality score). This lets an agent distinguish it conceptually from verification-style siblings like verify_work, though no sibling is named.

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 clause '这是AI评价而非已确认的来源身份或客观质量分数' implicitly separates it from verification/identity tools, and '必须关联该文献原文' states a precondition. However, there is no explicit when-to-use vs when-not framing and no alternative tool is named, so usage is only implied.

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