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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds that the payload is example/sample data rather than a real user result, which is useful context, but it says nothing about what the response contains or whether an example exists for every testId.

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 short sentence with zero filler, front-loading the action and resource before the purpose clause. Nothing to trim.

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 simple one-parameter read with no output schema, the definition covers the what and the why but not the return shape or behavior when no example exists for a given testId. Adequate but leaves the agent guessing about output content.

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?

Only one parameter with 100% schema description coverage; the schema already documents testId and provides example values (mbti-four-preferences, shadow16). The description adds no meaning beyond the schema, so the baseline 3 applies.

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 (读取/read) and resource (标明为示例的完成结果 — the completed result marked as example), which distinguishes it from get_results (real results) and get_profile_example (profile example). It does not explicitly name those siblings, so the differentiation is inferred rather than stated.

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?

"用于接入体验" implies the tool is for integration/onboarding trials rather than real data retrieval, which gives implicit usage context. It names no explicit alternatives (e.g., get_results for real data) and states no when-not conditions.

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