generate_ai_fix

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

With no annotations, the description carries the full burden and does well by disclosing issue-type-specific behavior (number of alternatives per type), auto-extraction of brand palette, and the ready-to-paste code output. It lacks mention of potential side effects or error states, but overall provides sufficient behavioral insight.

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 paragraph of moderate length that efficiently conveys key information. While not overly verbose, it could be slightly more structured (e.g., bullet points) for easier scanning. It remains concise without missing critical details.

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 tool's complexity (6 params, nested objects, no output schema), the description covers essential behavioral aspects: issue-type handling, auto-extraction, and output format. It references related tools (detect_framework) and provides enough context 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 coverage is 100%, so baseline is 3. The description adds value by explaining behavioral differences (e.g., brandColors auto-extraction) and how parameters interplay with issue type, going beyond what the schema provides.

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: generating framework-aware fix alternatives for a specific accessibility issue. It distinguishes itself from sibling tools by detailing different behaviors for color contrast vs. label/ARIA issues, making it unambiguous what the tool does.

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 clear context on when to use the tool (for a specific issue) and hints at prerequisites via the framework parameter (recommends using detect_framework first). However, it does not explicitly state when not to use it or name alternative tools for different scenarios, which would improve guidance further.

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