Analyze Flaky Tests

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that flaky tests are detected from CI run data, without mentioning how detection works, whether GitHub API access is needed, what input requirements exist beyond the schema, or any limitations.

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 with no filler or redundant phrasing. It is concise and immediately states the core action, though it is minimal enough that some behavioral and usage context is missing.

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 tool has only two parameters, one required, and an output schema exists, so the core invocation contract is fairly complete. However, the description is thin on selection guidance and behavior, which is notable given the large set of closely related sibling tools.

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%, so the baseline is 3; the schema already explains github_ref and run_ids. The description adds only a weak connection to 'CI run data' and does not enrich parameter meaning further.

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 identifies the specific verb 'Detect' and resource 'flaky tests' from 'CI run data', which distinguishes it from related sibling tools that address coverage, quality, or test patterns. It is not merely restating the title, though it stays close to the tool's name.

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 when-to-use or when-not-to-use guidance is provided. The description implies use for flaky-test detection but does not differentiate this from related siblings like detect_test_patterns or test_result_summary, leaving the selection decision to inference.

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