suggest_fix

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 states only that it 'suggests fixes' but does not mention whether it queries an external AI, what output format to expect, or any side effects. Minimal behavioral information is disclosed beyond the basic action.

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, compact sentence that is front-loaded with the core purpose. Every word contributes meaning, and there is no filler or 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?

The tool is simple (one parameter, no output schema), and the description adequately states its purpose. However, it does not describe the return value or output format, which is a notable gap given there is no output schema to fill that role. The description is minimally viable but not fully complete.

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%, as the errorMessage parameter is already described as 'Playwright or Automation error message'. The description adds no additional meaning about how the parameter should be formatted or used, 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 clearly states a specific action ('Suggest AI fixes') applied to a specific resource ('Playwright and automation failures'). This distinguishes it from sibling tools that analyze, classify, or generate tests, and the verb+resource pattern directly communicates the tool's function without ambiguity.

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 provides no guidance on when to use this tool versus alternatives like analyze_failure or classify_failure. It lacks any mention of prerequisites, conditions, or scenarios where this tool is preferred, leaving the agent to infer usage from the name alone.

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