matrix_scalar_multiply

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 only states the operation without detailing output format, error handling, performance implications, or any side effects. This is inadequate for a tool with parameters and no output schema.

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, consisting of a single sentence that directly states the tool's purpose. It is front-loaded with the core action and includes domain/category tags efficiently, with no wasted words or redundancy.

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 (2 parameters, 0% schema coverage, no annotations, no output schema), the description is severely incomplete. It lacks essential details on parameter semantics, behavioral traits, and expected outputs, making it insufficient for effective tool invocation.

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 'matrix' and 'scalar' are undocumented in the schema. The description adds no semantic information about these parameters, such as matrix format, scalar type constraints, or examples, failing to compensate for the schema gap.

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 verb ('Multiply') and resource ('a matrix by a scalar value'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'scalar_multiply' or 'element_wise_multiply', which could cause ambiguity in tool selection.

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 a domain ('linear_algebra.matrices') and category ('general'), but these are generic and do not specify usage context, prerequisites, or exclusions, leaving the agent without clear selection criteria.

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