manhattan_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 calculation action but does not describe output format, error handling, performance characteristics, or any side effects. For a computational tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.

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—a single sentence that directly states the tool's purpose. It is front-loaded with the core functionality and includes domain/category tags efficiently. There is no wasted verbiage, making it highly readable and to-the-point.

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 computational nature, lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It does not address parameter details, return values, error conditions, or usage distinctions from similar tools. For a tool with one parameter and no structured support, more context is needed to guide effective use.

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 schema description coverage is 0%, and the description does not mention the 'vector' parameter at all. It fails to explain what the vector input should contain (e.g., numeric values as strings), its expected format, or any constraints. With one undocumented parameter, the description adds no semantic value beyond the bare schema.

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 Manhattan norm (L1 norm) of a vector'. It specifies the verb ('calculate'), resource ('Manhattan norm'), and domain context ('linear_algebra.vectors'), but does not explicitly differentiate from sibling tools like 'euclidean_norm' or 'chebyshev_norm' beyond naming them differently.

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 minimal usage guidance. It mentions the domain ('linear_algebra.vectors') and category ('general'), but offers no explicit advice on when to use this tool versus alternatives like 'euclidean_norm' or 'p_norm', nor does it specify prerequisites or exclusions. The context is implied but not elaborated.

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