p_norm

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

No annotations are provided, so the description carries the full burden. It states the tool calculates a p-norm for p >= 1, implying a mathematical computation, but does not disclose behavioral traits such as error handling (e.g., for invalid inputs like non-numeric vectors or p < 1), performance characteristics, or output format. This leaves significant gaps for a tool with two required parameters.

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 concise and front-loaded: a single sentence that directly states the tool's purpose and domain. There is zero wasted text, and every word earns its place by conveying essential information efficiently.

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 tool's complexity (mathematical computation with two required parameters), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It fails to explain parameter details, behavioral expectations, or output format, leaving the agent with inadequate information to use the tool correctly beyond a basic understanding.

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 mentions 'vector' and 'p >= 1', adding basic semantics: p is a number >= 1, and vector is implied to be an array. However, it does not explain the vector's expected format (e.g., numeric values, handling of strings in the array) or constraints (e.g., p as integer vs. real). With two undocumented parameters, this is insufficient compensation.

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 the tool's purpose: 'Calculate the p-norm of a vector for any p >= 1'. It specifies the verb ('calculate'), resource ('p-norm of a vector'), and domain/category context. However, it does not explicitly differentiate from sibling tools like 'euclidean_norm' or 'manhattan_norm', which are specific cases of p-norms, so it misses full sibling distinction.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions the domain 'linear_algebra.vectors' and category 'general', but offers no explicit when/when-not instructions or references to sibling tools like 'euclidean_norm' (p=2) or 'manhattan_norm' (p=1), leaving usage context implied at best.

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