diagnose_frontend

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

No annotations are provided, so the description must convey behavioral traits. It mentions 'via GitLab search' but does not disclose whether the tool is read-only, requires authentication, or what it modifies. The lack of behavioral detail hampers safe invocation.

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 sentence that efficiently conveys the core purpose and method. It avoids unnecessary words, making it concise and easy to parse.

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?

With no output schema and no annotations, the description lacks details on return values, side effects, or preconditions. For a diagnostic tool that searches code, more context (e.g., what the output looks like) would improve completeness.

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 coverage is 100% with one parameter 'symptom' described in the schema as '症状描述 (如 ...)'. The description adds no new information beyond the schema, resulting in baseline score of 3.

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 '诊断前端渲染/Store/组件加载等问题 (通过 GitLab 搜索)' clearly states the tool's purpose: diagnosing frontend issues using GitLab search. However, it does not differentiate from siblings like diagnose_error or parse_stacktrace, which limits clarity in distinguishing use cases.

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?

No guidance on when to use this tool versus alternatives. Sibling tools include diagnose_error, parse_stacktrace, etc., but the description provides no context for selection, leaving the agent to infer usage.

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