matrix_decompose

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It fails to specify what decompositions are computed (e.g., LU, QR, SVD), input shape requirements, or impact on the input. The word 'compute' implies a read operation, but no safety details are given.

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 a single sentence, efficient and to the point. However, it sacrifices necessary detail for brevity, making it slightly under-specified for its purpose.

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 complexity of matrix decompositions and the lack of schema descriptions, the description is insufficient. It does not address output format, error conditions, or limitations. The presence of an output schema does not excuse the lack of clarity in the description.

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 description coverage is 0%, so the description must compensate. It does not explain the 'matrix' parameter's shape constraints or the 'decomposition' parameter's allowed values (e.g., 'all', 'lu', 'qr'). The default 'all' is not defined. This is a critical gap.

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 'Compute standard matrix decompositions' clearly states the verb and resource, distinguishing from sibling tools like eigen_decomp which focus on a specific decomposition type. However, 'standard' is vague and could be more precise.

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 alternatives such as eigen_decomp or matrix_info. There is no mention of prerequisites, input constraints, or recommended scenarios.

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