chebyshev_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 of behavioral disclosure. It states the tool calculates a norm, implying a read-only operation, but does not specify output format, error handling, or performance characteristics (e.g., computational complexity). For a tool with no annotations, this is a significant gap, as the agent lacks insight into what to expect beyond the basic operation.

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 concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no unnecessary verbosity or redundant information. However, it could be more structured by separating usage context or parameter details, but given its brevity, it efficiently conveys the core function without 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 the complexity of a mathematical norm calculation, the description is incomplete. There are no annotations, no output schema, and low parameter coverage. The description does not explain the return value (e.g., a numeric result), error cases, or mathematical assumptions. For a tool with rich sibling context (e.g., other norm tools), more detail is needed to ensure the agent can use it correctly.

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?

The input schema has 0% description coverage, with one parameter 'vector' of type array of strings. The description does not add any semantic details about this parameter, such as what the strings represent (e.g., numeric values), expected format, or constraints (e.g., non-empty array). With low schema coverage, the description fails to compensate, leaving the parameter poorly documented.

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 Chebyshev norm (L∞ norm) of a vector'. It specifies the verb ('calculate'), the mathematical operation ('Chebyshev norm'), and the input ('vector'), which is specific and unambiguous. However, it does not explicitly differentiate from sibling tools like 'euclidean_norm' or 'manhattan_norm', which are also vector 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 this is too vague to inform usage decisions. There is no mention of when Chebyshev norm is preferred over other norms (e.g., Euclidean or Manhattan), nor any prerequisites or exclusions, leaving the agent without practical usage context.

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