impact_analysis

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

No annotations are provided, so the description carries the full burden. It discloses that it returns a priority-sorted list of affected test cases and mentions the config file rules. It could be improved by noting any potential side effects, but for a read-only analysis tool, the information is adequate.

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 concise but well-structured with usage examples and prerequisites. Every sentence adds value, and the bullet-point format enhances readability.

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?

Given the rich input schema and no output schema, the description provides sufficient context: it explains the return type (priority-sorted list), includes prerequisites, and offers multiple usage patterns, making it complete for an AI agent to correctly invoke the tool.

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 input schema covers 100% of parameters, so the baseline is 3. The description adds value by explaining the mutual exclusivity of 'name' and 'module', and through examples, it clarifies optional parameters and default behaviors (watermark, HEAD).

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 '分析代码变更对测试用例的影响' (analyze impact of code changes on test cases), which is specific and distinguishes from sibling tools like repo_clone, repo_monitor, risk_assessment, test_recommendation, and TIA-init.

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 provides usage examples with different parameter combinations (name, from-to, module) and lists prerequisites (repo_monitor initialized, config file edited), making it clear when and how to use the tool.

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