validate_optimization_input

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

No annotations are provided, so the description must fully disclose behavioral traits. It indicates the return includes errors, warnings, and suggestions, but does not state whether the tool is read-only, has side effects, or any other important behavioral characteristics.

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 very short and efficient, but includes parameter docstrings that could be better placed in the schema. It is front-loaded with the purpose but could be more structured.

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 (validation with two parameters, no output schema, 0% schema coverage), the description is incomplete. It does not specify the validation logic, the format of the return value, or how to use the results effectively.

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 compensate. It briefly explains problem_type and input_data but lacks details on valid values for problem_type or structure of input_data. This adds minimal meaning beyond the parameter names.

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 that the tool validates input data for optimization problems. While it doesn't explicitly differentiate from sibling tools, the siblings are all problem-solving tools, so the purpose is distinct and clear.

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 that this tool should be used to validate input before running optimization, but it does not provide explicit guidance on when to use it versus alternatives, nor does it mention prerequisites or typical workflows.

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