np_svd

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

No annotations exist, and the description does not disclose behavioral traits such as computational cost, matrix shape requirements, or output structure. With an output schema present, describing the return format would be valuable, but it is omitted.

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 (three words), lacking essential details. While minimal, it under-specifies the tool's behavior and outputs, making it insufficient for an agent to use correctly.

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?

Despite having an output schema (not detailed in the description), the description omits key context: what the decomposition returns, how to interpret results, and typical usage patterns. For a mathematical tool like SVD, this is incomplete.

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 the input schema already documents the two parameters ('array' and 'full_matrices'). The description adds no additional semantics beyond the schema, which is adequate but does not clarify default behavior or parameter impact.

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 'Singular Value Decomposition' clearly states the operation (decompose a matrix), but it lacks differentiation from sibling tools like np_eig, np_det, or np_inv. It does not specify what is returned (U, S, Vt) or how it differs from other decompositions.

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 SVD versus alternatives (e.g., eigenvalue decomposition, QR decomposition). There are no context cues, prerequisites, or examples indicating appropriate use cases.

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