analyze_generation_feedback

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

No annotations are provided, so the description must fully convey behavioral traits. It states the tool returns feedback (a read operation), but does not explicitly confirm it is non-destructive, nor does it mention any side effects, authentication needs, or rate limits. The description is insufficient given the lack of annotations.

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 front-loaded with a clear purpose statement and a return value description, followed by a structured parameter list. It is reasonably concise, though the parameter list is somewhat verbose. Overall, it is well-organized and efficient.

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?

For a tool with 8 parameters, no output schema, and many siblings, the description provides basic functionality and parameter info but lacks return value format details, error conditions, or usage context. It does not fully compensate for the missing output schema or annotations, leaving gaps for an agent.

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 0%, so the description must provide parameter meaning. It includes a docstring listing each parameter with a brief explanation (e.g., failure_mode: 'Optional failure mode if the model was printed and failed'). These add value beyond the schema's titles and types, though descriptions are minimal. The effort compensates for the lack of schema descriptions.

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 analyzes a generated model to get feedback for improvement, specifying it returns constraints to add to the generation prompt. This verb+resource pairing is specific and distinguishes it from sibling analysis tools like analyze_print_failure or analyze_warping_risk.

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 implies the tool is used when you have a generated model to improve, but it lacks explicit guidance on when to use it versus alternatives (e.g., other analysis tools). No when-not-to-use or prerequisite information is provided.

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