dot_product

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. The description only states what the tool calculates, without mentioning behavioral aspects 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 that performs a mathematical 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. The domain/category tag is appended efficiently. There is no wasted verbiage, making it easy to parse, though it could benefit from more detail given the lack of annotations and schema descriptions.

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 operation with no annotations, 0% schema description coverage, and no output schema, the description is incomplete. It fails to address key contextual elements such as parameter details, behavioral traits, error conditions, or output expectations. This makes it insufficient for an AI agent to reliably invoke the tool without additional assumptions.

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 does not explain what 'vector_a' and 'vector_b' represent (e.g., arrays of numbers), their expected format (e.g., numeric strings), constraints (e.g., equal length), or examples. This is inadequate given the low schema coverage and the tool's reliance on correct parameter interpretation.

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 dot product of two vectors (inner product)'. It specifies the verb ('calculate'), resource ('dot product'), and provides the synonym 'inner product' for clarity. However, it does not explicitly differentiate from sibling tools like 'cross_product' or 'scalar_projection', which are related vector operations.

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 includes a domain/category tag ('Domain: linear_algebra.vectors, Category: general'), which implies context but does not specify when to use this tool versus alternatives like 'cross_product' or 'scalar_projection'. No explicit when-to-use, when-not-to-use, or prerequisite information is given.

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