np_linalg_norm

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

With no annotations, the description must disclose behavioral traits. It states the core function, but does not mention that the operation is read-only, non-destructive, or its mathematical nature. However, the presence of an output schema reduces the need to explain return values. The description does not contradict any annotations since none exist.

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 concise single phrase. It is front-loaded and uses every word to convey the essential purpose. While it could be slightly more informative, it avoids verbosity.

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 norms (various ord options, differing defaults for vectors vs matrices), the description is far too minimal. It does not explain that the default ord depends on input dimensionality or list the supported norms, though the schema partially remedies this.

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 100%, so the baseline is 3. The description adds no additional meaning beyond the schema for the 'array' and 'ord' parameters, merely repeating the concept.

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 'Matrix or vector norm' clearly identifies the operation as computing a norm for either type of input. It is a specific verb+resource, but does not differentiate from similar linear algebra tools like np_det or np_svd, which keeps it from a perfect score.

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 np_linalg_norm versus other norms or linear algebra functions. There is no mention of prerequisites, alternatives, or context for the ord parameter beyond the schema.

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