vector_dot_product

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

With no annotations present, the description carries the full burden and does reasonably well: it discloses the success shape ({"result": scalar}), the error shape ({"error": message}), the equal-length precondition, and supplies a worked example. It does not state what specifically triggers the error case (e.g., mismatched lengths) or behavior for empty vectors.

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?

Front-loaded one-line summary followed by Args, Returns, and a runnable example — every section earns its place and there is no filler. Especially valuable given there is no output schema, making the Returns block necessary rather than redundant.

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?

For a two-parameter pure function with no annotations and no output schema, the description covers purpose, inputs, return value, error path, and an example. The only gaps are edge-case semantics (mismatched lengths, empty vectors) and the absence of any cross-reference to sibling vector operations.

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%, so the description must compensate, and it documents both parameters as lists of numbers. That exceeds the schema's bare names/types, though it adds no constraints beyond 'equal length' and no examples of parameter values (the example shows them only inside a combined call).

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 names a precise operation on a specific resource ('dot product of two equal-length vectors'), and the equal-length constraint adds real information. It never differentiates itself from siblings like vector_cross_product or vector_magnitude, so an agent relying on the text alone must infer the distinction from the names.

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?

Usage is only implied by the mathematical semantics — there is no explicit guidance on when to choose the dot product over vector_cross_product or vector_magnitude. For a pure, unambiguous math operation this is tolerable, but no alternatives or conditions are stated.

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