sum

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

No annotations are supplied, so the description carries the full behavioral burden and does not meet it. It says nothing about determinism, behavior on an empty array, non-numeric handling, or overflow, all of which matter for an aggregation over arbitrary input values.

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?

A single compact sentence with no filler, and the verb is front-loaded. It is efficient, though it is arguably under-specified rather than optimally trimmed.

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?

An output schema exists, so return-value explanation is not needed, and the tool takes one required parameter. Still, the absence of any disambiguation from 'add' or guidance on edge-case input leaves the definition only minimally viable for a math primitive in a dense sibling set.

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 for the single 'numbers' parameter. Saying 'all values in a dataset' does clarify that the input is a whole collection to be collapsed rather than two operands, which is more than the bare parameter name conveys, but it adds no format, type, or ordering detail.

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 a specific verb ('Calculate') and resource ('sum of all values in a dataset'), which is clearer than a tautology. However, it does not distinguish this from siblings that are semantically adjacent, most notably 'add' (binary addition) versus 'sum' (aggregate over a collection), nor does it clarify its relationship to aggregate siblings like 'mean', 'count', or 'max_value'.

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?

There is no statement of when to use this tool instead of 'add' or the other aggregate siblings. An agent must infer from the word 'dataset' that this collapses an entire collection rather than adding two operands, which is a meaningful ambiguity in a 23-tool arithmetic family.

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