vector_add

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 operation ('Add two vectors element-wise') but does not describe any behavioral traits such as input validation (e.g., vectors must be of equal length), error handling, performance characteristics, or output format. This leaves significant gaps for a tool with no annotation coverage.

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, followed by domain/category information. There is no wasted verbiage, and every part of the sentence contributes to understanding the tool's function.

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 (a mathematical operation with two parameters), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It does not provide enough context for safe and correct usage, such as parameter details, behavioral expectations, or output information. The domain/category hint is insufficient to fill these gaps.

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, and the description does not add any parameter semantics. It mentions 'two vectors' but does not explain what 'vector_a' and 'vector_b' represent, their expected formats (e.g., arrays of numbers), constraints (e.g., must be same length), or examples. With low schema coverage, the description fails to compensate for the lack of parameter documentation.

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: 'Add two vectors element-wise' with a specific verb ('Add') and resource ('vectors'), and it includes domain/category context ('Domain: linear_algebra.vectors, Category: general'). However, it does not explicitly distinguish this from sibling tools like 'matrix_add' or 'add', which might handle different types of addition, so it misses full sibling differentiation.

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') but does not specify when this tool is appropriate compared to other addition-related tools in the sibling list (e.g., 'add', 'matrix_add', 'element_wise_add'). There is no explicit when/when-not or alternative tool naming.

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