sympy_matrix_eigenvals

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

With no annotations provided, the description carries full burden of behavioral disclosure. It fails to mention that the tool works on symbolic matrices, what happens for non-square matrices (likely error), or any side effects. The output schema exists but is not described, leaving behavior opaque.

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 extremely short and to the point, with no unnecessary words. However, it could be structured with more detail in a few sentences without sacrificing conciseness.

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 simplicity of the tool and presence of an output schema, the description is insufficient. It does not explain the input format (e.g., string representation of a matrix), nor does it address edge cases like non-square matrices or eigenvalues in a different field. Agents need more 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?

The single parameter 'matrix' is described as 'Matrix string' in the schema, and the description adds no further detail. Since schema coverage is 100%, baseline is 3; no extra value is provided beyond the schema.

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 it computes eigenvalues of a matrix. However, given the sibling sympy_matrix_eigenvects which computes both eigenvalues and eigenvectors, it lacks differentiation that would help an agent choose between them.

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?

No guidance is provided on when to use this tool versus sympy_matrix_eigenvects or other matrix tools. There is no mention of prerequisites, limitations, or alternative tools.

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