flakiness_report

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

With no annotations, the description carries the full burden. It explains the classification logic, the min_runs threshold, and the return structure. It does not mention any destructive actions, authentication needs, or rate limits, but for a read-only analysis tool, the provided 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 well-structured with a summary line, usage context, and clearly separated args/returns. It is concise and contains no unnecessary words.

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 existence of an output schema, the description provides a sufficient summary of return values and includes all necessary input details. The classification logic and ordering are explained, making it complete for an agent to use 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?

The input schema has 0% description coverage, but the description adds meaning for both parameters: path_glob is explained with an example and note on alphabetical order, min_runs is explained with default value. This fully compensates for the missing schema descriptions.

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: classifies tests as stable, flaky, or broken from multiple successive JUnit reports. It specifies the verb 'clasifica' and resource 'tests', and implicitly distinguishes from sibling tool 'parse_junit' which handles single reports.

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 when to use: when you have several successive JUnit reports from the same suite. It provides context about the classification output and mentions 'datos_insuficientes' for cases with insufficient runs, but does not explicitly state when not to use or directly name alternatives.

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