matrix_subtract

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 states the operation is 'element-wise', which clarifies the subtraction method, but it does not cover other critical aspects: it does not mention input validation (e.g., matrix dimensions must match), error handling, performance characteristics, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 and front-loaded: it directly states the tool's purpose in a single, clear sentence. The additional domain and category information is minimal and does not detract from clarity. There is no wasted verbiage, making it efficient for an agent to parse.

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 context: no annotations, no output schema, and 0% schema description coverage for two parameters, the description is incomplete. It adequately explains the core operation but lacks necessary details about parameter semantics, behavioral traits (e.g., error conditions), and output expectations. For a tool performing matrix subtraction, which involves non-trivial constraints like dimension matching, this leaves the agent under-informed.

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%, meaning the input schema provides no descriptions for parameters. The tool description does not add any information about the parameters 'matrix_a' and 'matrix_b', such as their expected formats (e.g., 2D arrays of numbers), constraints (e.g., must be same shape), or examples. With two required parameters and no semantic details provided, the description fails to compensate for the lack of schema documentation.

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's purpose: 'Subtract one matrix from another element-wise'. It specifies the verb ('subtract'), resource ('matrices'), and operation type ('element-wise'), which is specific and unambiguous. However, it does not explicitly differentiate from sibling tools like 'matrix_add' or 'vector_subtract', which are similar operations on different data structures, so it falls short of a perfect score.

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 the domain ('linear_algebra.matrices') and category ('general'), but this does not help an agent choose between this tool and other matrix or subtraction-related tools in the sibling list, such as 'matrix_add' or 'subtract'. There is no explicit when-to-use or when-not-to-use information.

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