math_add

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the fundamental operation but does not explicitly describe the return value or mention edge cases such as type handling beyond the schema's numeric type requirement.

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 with no filler. Every word contributes meaning, and it is immediately scannable.

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 low complexity of the tool, the required numeric schema, and the clearly stated operation, the description is mostly complete. The only minor gap is that the return value is not explicitly described, though it is easily inferred as the sum.

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%, so the description must compensate. 'Add two numbers' indirectly clarifies that parameters a and b are the addends, but it adds no per-parameter details beyond what the parameter names and types already imply.

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 specifies a clear verb ('Add') and resource ('two numbers'), making the tool's core operation unambiguous. It distinguishes from sibling operations like math_divide and math_multiply by the stated operation, though not explicitly mentioning them.

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 intended usage is implied: use this tool when two numeric values need to be summed. There is no explicit guidance about when not to use it or how it compares to alternative arithmetic tools.

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