Matrix Eigenvalues

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

Description adds value beyond annotations by noting NumPy requirement and providing examples. No contradiction with readOnlyHint and idempotentHint.

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?

Description is brief, front-loaded with action, includes necessary note and examples. Every sentence is useful, no waste.

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 full schema coverage and presence of output schema, description is complete. It explains tool purpose, dependency, and provides examples.

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. Description includes examples but does not add new parameter semantics beyond 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?

Description clearly states 'Calculate the eigenvalues of a square matrix', which is a specific verb+resource. It distinguishes from sibling matrix tools like determinant and inverse.

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?

Provides context on required dependency (NumPy) and failure mode (ValueError). Lacks explicit 'when not to use' but purpose is clear enough to infer appropriate usage.

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