Summarize List

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

Annotations are absent, so the description carries the full burden. It explicitly states it computes statistics, implying a pure, non-mutating operation. However, it does not disclose edge-case behavior (e.g., NaN, Infinity) or side effects, but for a simple computation tool, this is adequate.

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, well-organized sentence that front-loads the purpose and lists all statistics, with no wasted words or redundancy.

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 tool is simple (one parameter) and has an output schema, so return values don't need to be in the description. The description covers purpose and parameters sufficiently, but lacks explicit usage context; still, it is complete enough for a tool of this complexity.

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 input schema already has 100% coverage, describing 'numbers' as 'A non-empty list of numbers'. The description repeats 'list of numbers' without adding any additional parameter semantics, so it meets the baseline of 3 but adds no extra value.

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 uses a specific verb 'computes' and clearly specifies the resource 'a list of numbers' along with the exact statistics (count, sum, min, max, mean, median, standard deviation). This fully distinguishes it from sibling tools like 'calculate' or 'random_number'.

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 implies usage for summarizing numeric lists but does not explicitly state when to use it over alternatives like 'calculate' or exclude non-list cases. No direct comparison or when-not-to-use guidance is provided.

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