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

Annotations already declare readOnlyHint and idempotentHint. The description adds meaningful context by stating that NumPy is required and that a ValueError is raised if unavailable, which is a behavioral trait not covered by annotations. Examples further demonstrate usage, though non-square matrix behavior is not explicitly disclosed.

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 concise and front-loaded with the core purpose. The note about NumPy and the examples are useful and non-redundant, making every sentence earn its place without unnecessary fluff.

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 simple tool with a well-documented schema and output schema, the description covers purpose, prerequisite, and examples. It would benefit from explicit mention of behavior for non-square inputs, but given the annotations and schema richness, it is adequately 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?

The input schema provides 100% coverage, describing the matrix parameter as a 2D list of numbers representing a square matrix with an example. The description's examples reiterate this but do not add new meaning beyond the schema, so it meets the baseline for high schema coverage.

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 states 'Calculate the determinant of a square matrix' with a specific verb and resource, clearly distinguishing it from sibling matrix tools like matrix_inverse or matrix_eigenvalues. The purpose is unambiguous and action-oriented.

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 usage for determinant calculation but provides no explicit guidance on when to choose this over other matrix operations. It includes practical examples and a NumPy prerequisite, but lacks alternative comparisons or exclusions, so guidance is only implied.

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