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

No annotations are provided, so the description carries the full burden of behavioral disclosure. Its only behavioral cue is the ambiguous "[PUBLIC READ/WRITE]" tag; it does not state whether the calculation is pure/read-only, whether it has side effects, or what permissions are needed.

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 front-loaded sentence with no padding. The bracketed "[PUBLIC READ/WRITE]" tag is vague but very brief, so it does not significantly hurt conciseness.

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 schema documents both inputs and the description names the outputs, which is minimally adequate for a low-complexity calculation tool. However, with no annotations and no output schema, it should clarify the optional-parameter selection and return format more explicitly.

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 explains both parameters and the baseline is 3. The description adds no parameter-level meaning beyond the schema, such as when to use text versus probabilities or whether one of the optional inputs must be supplied.

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 and resource: calculating Shannon entropy, state uncertainty, and Information Gain ratio. It is clear enough to distinguish the metric from calculators like calculate_wasserstein_distance, but it does not explicitly differentiate itself from the many other math-computation siblings.

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 guidance on when to use this tool versus compute_math_expression, solve_symbolic_matrix, or calculate_wasserstein_distance. It also does not say whether callers should provide text, probabilities, or both, which is important because neither parameter is required.

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