detect_flaky_tests

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

No annotations were provided, so the description carries the full burden. It discloses that the tool runs the full suite multiple times (potentially expensive), uses stable pytest node IDs, controls test order and seeds, and reports flakiness rates. However, it does not mention if the tool modifies any files or has any side effects beyond reporting.

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 and structured effectively. The first sentence immediately states the core purpose, followed by a short paragraph explaining the mechanism and guarantees. Every sentence adds essential information 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?

Given that the tool has only 2 parameters with 100% schema coverage, an output schema exists, and no annotations are provided, the description sufficiently covers what the tool does, how it works, and what guarantees it provides (controlled order/seeds). There are no obvious gaps.

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%, so the baseline is 3. The description adds value by explaining that runs controls the number of executions and that higher values give more confidence but take proportionally longer. It also clarifies that repo_path must be a Python repository and resolves under allowed roots. The first sentence of the description contextualizes both parameters together.

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 using a specific verb-resource pair: 'Run the test suite multiple times and flag tests with inconsistent outcomes.' It immediately distinguishes itself from siblings by focusing on flakiness detection rather than coverage analysis, risk prediction, or history retrieval.

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 implicitly suggests when to use this tool (to identify flaky tests) vs alternatives (e.g., analyze_coverage for coverage, get_test_history for historical results). It explains that test order and seeds are controlled to avoid false positives, but does not explicitly state when NOT to use it or provide direct comparisons to siblings.

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