搜索牛客网

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

Annotations already declare readOnly/idempotent/openWorld, and the description adds real behavioral context beyond them: timeout handling, network-failure messaging, and the no-results suggestion. This is meaningful disclosure for a scraper-style tool that isn't captured by the annotations.

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?

Purpose is front-loaded in the first sentence, then organized into Args/Returns/Examples/Error Handling sections. Slightly verbose, but each block carries distinct information and nothing is wasted.

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?

There is no output schema, and the description compensates by listing returned fields (标题、链接、摘要、作者、时间) plus error behavior. For a 4-param, single-required search tool this is complete enough to invoke 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 100%, with enums and descriptions already documenting every parameter including defaults. The description's Args block largely restates the schema; the examples add combination context but no new parameter syntax or constraints, so 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 (搜索) and resource (牛客网内容) and enumerates the content types it can search (讨论帖、面经、编程题、职位). This distinguishes it from the narrower siblings like nowcoder_get_problem and nowcoder_list_problems, though it never names them explicitly.

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?

Usage is conveyed only implicitly through the examples (Java面经 → discuss, 二叉树 → problem), which shows how the type filter maps to intent. There is no explicit statement of when to prefer this search tool over the more specific get/list siblings, nor any exclusion guidance.

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