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

With no annotations, the description carries the full burden of behavioral disclosure. 'Compute' implies a pure calculation, but the description does not explain input format expectations, tie handling, multiple modes, empty datasets, or return value shape.

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 words. While it is minimal, it is also appropriately front-loaded for the core purpose; slight redundancy comes from the 'Mode:' prefix echoing the tool name.

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?

For a simple one-parameter tool, the description still leaves critical ambiguity: the data parameter is a string but its expected format is not specified, and there is no output schema or mention of return behavior. An agent may not know whether to pass '1,2,2,3' or '[1,2,2,3]'.

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 100%, so the baseline is 3. The tool description adds only marginal value by equating 'data' with 'dataset', but it does not clarify what string representation the dataset should take, such as comma-separated values or a serialized array.

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 operation, 'Compute the mode of a dataset', with a clear verb and resource. It is not a tautology, though it does not explicitly differentiate itself from statistical siblings like get_stats or median.

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 does not mention related statistical tools, exclusions, or scenarios where another tool would be better.

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