matrix_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 basic function without any details on error handling (e.g., for incompatible dimensions), performance characteristics, output format, or computational limits. This is inadequate for a tool that performs mathematical operations with potential constraints.

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 a single, efficient sentence in Chinese that directly states the tool's purpose without any fluff. It's front-loaded and wastes no words, making it highly concise and well-structured for its minimal content.

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 matrix multiplication (involving dimension compatibility and mathematical operations), lack of annotations, 0% schema coverage, and no output schema, the description is severely incomplete. It fails to address key contextual aspects like input formats, error conditions, or result details, making it inadequate for effective tool use.

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 schema description coverage is 0%, with two parameters (matrix_a and matrix_b) having no schema descriptions. The description adds no semantic information about parameters—it doesn't specify that matrices should be 2D arrays, mention dimension requirements, or provide examples. With low coverage and no compensation in the description, this is insufficient.

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 '计算两个矩阵的乘积' (calculates the product of two matrices) clearly states the verb (calculate) and resource (matrix product). It distinguishes from siblings like matrix_add, matrix_subtract, matrix_inverse, etc., by specifying multiplication rather than other matrix operations. However, it doesn't explicitly mention that it's for numerical matrices or contrast with scalar multiplication (handled by 'multiply'), so it's not fully specific.

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 doesn't mention prerequisites (e.g., matrix dimensions must be compatible), exclusions (e.g., not for scalar multiplication), or refer to sibling tools like matrix_add for other operations. Usage is implied by the name but not explicitly stated.

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