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

Annotations already declare readOnlyHint and idempotentHint, and the description adds value by disclosing the NumPy dependency and the ValueError condition if unavailable. This is useful behavioral context beyond the structured annotations, though it does not cover all error cases like dimension mismatches.

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 compact and front-loaded with the core purpose, followed by a critical dependency note and two concise examples. No wasted sentences; the formula and examples earn their place.

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?

With annotations, output schema, and full schema parameter coverage, the description sufficiently explains the tool's operation. The key missing explicit detail is the matrix dimension compatibility rule (A columns = B rows), though the examples illustrate it implicitly.

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 already provides 100% description coverage for both parameters with examples. The description's examples reinforce valid shapes but add no new semantic information beyond what the schema already offers, so the baseline of 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?

The description states 'Multiply two matrices (A × B)' with a specific verb and resource, clearly distinguishing this from sibling matrix tools like determinant, inverse, and transpose. The formula notation and examples reinforce the exact operation.

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?

Examples imply when to use this tool (valid multiplications with compatible dimensions) but there is no explicit guidance on when to choose this over sibling matrix operations or exclusions. The context is clear for a simple math function but lacks 'use this when' statements.

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