mark_false_positive

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

Annotations are absent, so the description must disclose side effects. It only states the action without explaining what changes occur, whether it is reversible, or how it affects downstream analysis. This is insufficient for a mutation tool.

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, clear sentence with no redundant information. Every word earns its place, and the structure is straightforward.

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 lack of annotations and output schema, the description is too terse. It does not clarify the operational impact (e.g., whether marked detections are excluded from get_loops or export_eval_cases), leaving the agent uncertain about side effects and downstream behavior.

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 schema fully describes the only parameter with 100% coverage, and the description does not add any additional nuance about how pattern_id relates to the action. The baseline score of 3 applies because the schema already documents the parameter.

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 the specific verb 'mark' with the resource 'loop detection' and the qualifier 'as a false positive', clearly distinguishing it from sibling tools like detect_loops and get_loops.

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 guidance is provided on when to use this tool versus alternatives such as annotate_event, nor any prerequisites or expected workflow context. The description does not mention any exclusions or distinctions.

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