flaky_detect_patterns

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It indicates static analysis of test code but does not say whether the operation is read-only, what the response contains, whether the code is executed, or how the api_key is used. This leaves important behavioral traits undisclosed.

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?

A single, front-loaded sentence with no filler words. It efficiently communicates the tool's purpose, though it is brief enough that some behavioral context is missing. Conciseness itself is strong.

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?

With no output schema and no annotations, the description should explain more about expected results, authentication requirements, and constraints on test_code. It does not describe return values or how framework influences analysis, leaving the agent with an incomplete picture for correct invocation.

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 input schema fully documents api_key, framework, and test_code. The description adds a general mention of 'test code' but no additional semantic value beyond what the schema already provides, so the baseline of 3 applies.

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 uses a specific verb ('Analyze') and resource ('test code') and states the goal: detecting common flaky test patterns and anti-patterns. This is clear and generally distinguishable from sibling tools like flaky_diagnose_root_cause or flaky_fix_suggestions, though it does not explicitly differentiate itself.

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?

There is no guidance on when to use this tool versus alternatives such as flaky_diagnose_root_cause or flaky_fix_suggestions. The description only states what the tool does, leaving the agent to infer use cases without any exclusions or selection criteria.

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