vector_subtract

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 ('Subtract one vector from another element-wise') but does not describe critical behaviors such as input validation (e.g., vector length matching), error handling, or output format. For a tool with no annotations, this is a significant gap in transparency.

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 with the core purpose in a single sentence. There is no wasted text, and the domain/category hints are efficiently appended. It earns its place by being direct 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 complexity (vector operation with two parameters), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It does not cover parameter details, behavioral traits, or output expectations, making it inadequate for an AI agent to use the tool effectively without additional context.

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%, meaning parameters are undocumented in the schema. The description does not add any parameter semantics—it does not explain what 'vector_a' and 'vector_b' represent, their expected formats (e.g., arrays of numbers), or constraints (e.g., same length). This fails to compensate for the lack of schema 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: 'Subtract one vector from another element-wise.' It specifies the verb ('Subtract'), resource ('vectors'), and operation type ('element-wise'), which is specific. However, it does not explicitly distinguish it from sibling tools like 'subtract' (scalar subtraction) or 'matrix_subtract', though the domain hint ('linear_algebra.vectors') provides some context.

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 does not specify use cases, prerequisites, or comparisons to sibling tools like 'subtract' or 'vector_add', leaving the agent without explicit usage instructions.

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