dot_product

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses what is returned (magnitude, angle, orthogonality check), which is useful, but says nothing about length-mismatch handling or error behavior. For a simple deterministic pure function the risk surface is small, so a 3 is reasonable.

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?

Two tight sentences with the primary action front-loaded and the return summary following. No filler, though it is minimal rather than rich.

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?

There is no output schema, so the description's disclosure of the return contents (magnitude, angle, orthogonality check) is genuinely necessary and present. Combined with fully documented params, this is essentially complete for a simple two-vector computation.

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 coverage is 100%, with both 'a' and 'b' documented including the same-length constraint on 'b'. The description adds no parameter-level meaning beyond the schema, so the baseline 3 applies.

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?

States a specific verb ('Compute') and resource ('dot product of two vectors'), clearly distinguishing this operation from the sibling cross_product. It does not explicitly name alternatives, but the operation itself is unambiguous and self-identifying.

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?

Provides no guidance on when to use this versus alternatives like cross_product, matrix_operate, or setops_calculate. No prerequisites or context conditions are stated, leaving selection to the model's inference.

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