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

With no annotations, the description carries the full behavioral burden, yet it only adds an ambiguous '[PUBLIC READ/WRITE]' access tag — odd for what is evidently a pure computation. It does not say whether the distance is symmetric, whether inputs must be normalized/same length, how costs are handled, or what is returned.

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, tightly front-loaded sentence with no filler; the access tag leads and the purpose follows. Nothing is wasted, though the brevity comes at the cost of missing constraints.

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?

No output schema exists, so the description should at minimum state that a scalar distance is returned and describe input constraints such as equal length or normalization. Neither is present, and with zero annotations the definition is not sufficient to call the tool confidently.

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 schema already documents both parameters as distribution/embedding vectors. The description adds only that the inputs are expected to be probability distributions, which implies normalization but does not state it explicitly or address differing lengths/weights.

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?

States a specific verb ('Compute') and a specific metric ('Wasserstein-1D optimal transport distance') over a named resource (state probability distributions). It is clearly distinguishable from siblings such as calculate_entropy_gain and compute_math_expression, though it never explicitly contrasts itself with 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?

There is no statement of when this metric is the right choice versus alternatives like calculate_entropy_gain, no prerequisites, and no exclusions. The phrase 'between state probability distributions' only hints at the intended input domain.

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