Matrix Operations

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

Annotations already declare readOnlyHint and idempotentHint. The description adds behavioral context by providing concrete examples for each operation, showing input formats and results. It does not contradict annotations and adds value beyond them.

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 well-structured with clear headings for each operation and concise examples. While somewhat lengthy, each example serves a purpose and improves clarity. It is not overly verbose.

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?

The description covers all operations with examples but lacks mention of constraints such as square matrix requirement for inverse or determinant. Given the presence of an output schema, return values are not required. Overall adequate but has gaps.

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?

All 5 parameters have schema descriptions, giving a baseline of 3. The description adds significant value by providing specific examples of how each parameter is used, e.g., showing matrix1 and matrix2 values for multiplication, and the result format.

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 tool performs core matrix operations using NumPy BLAS and lists specific operations: multiply, inverse, transpose, determinant, and trace. This distinguishes it from sibling tools like matrix_decomposition and solve_linear_system.

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 explicit guidance on when to use this tool over alternatives such as matrix_decomposition or solve_linear_system. Usage is only implied by the listed operations, but there is no when-to-use or when-not-to-use advice.

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