Bug impact scan

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

Annotations already declare readOnlyHint=true, so the tool is safe. The description adds behavioral context by explaining the ranking method (keyword overlap and shared product), which helps the agent understand what the tool does beyond the schema. No contradictions with 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?

The description is very concise with only two sentences. The first sentence defines the action, and the second provides usage guidance. Every word contributes value without redundancy.

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?

The description covers the core purpose and usage but lacks details about the output format (e.g., how ranks are presented, what fields are returned). Given the tool has no output schema, the description could be more informative about the expected return value, which is a gap.

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?

The description adds meaning beyond the schema by explaining the two modes: using a bug ticket or providing an ad-hoc title/description. It also mentions 'shared product' which relates to the required 'project' parameter. Schema coverage is only 25%, so the description compensates well.

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 clearly states the tool's purpose: given a bug, rank existing features by impact using keyword overlap and shared product. It uses a specific verb ('rank') and resource ('features'), and it distinguishes itself from sibling tools like 'log_bug' or 'get_regressions' by focusing on impact scanning.

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 explicitly says 'Use it when logging a bug to spot regressions and linkage candidates,' providing a clear usage context. However, it does not mention when not to use this tool or list alternatives, which would strengthen guidance.

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