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

Annotations already declare this a read-only, idempotent, closed-world computation, so the safety profile is covered. The description adds the return shape ({probability}), but since an output schema exists that is largely redundant, and nothing is said about the d6 default or the 1-20 dice cap handling.

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?

Two tightly written sentences with the core purpose front-loaded and no redundancy. The trailing list_bundles pointer is marginally useful routing rather than filler.

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?

Given full schema coverage, an output schema, and read-only annotations, the description supplies enough for correct invocation. Only marginal context (dice/sides defaults, relationship to similar probability tools) is absent.

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 coverage is 100% and each parameter is documented, so the baseline is 3. The description's restatement of the comparison modes ('exact values, minimum or maximum targets') mirrors the enum rather than adding constraints or formatting detail beyond it.

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+resource ('Calculate dice roll probability') and enumerates the supported comparison modes, so the agent knows exactly what it computes. It does not, however, distinguish itself from nearby probability siblings such as calculate_card_draw_probability or calculate_probability_binomial, other than a generic pointer to list_bundles.

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?

Usage is implied by the three modes named (exact, minimum, maximum targets) and the list_bundles cross-reference gives ecosystem context, but there is no explicit when-to-use vs when-to-prefer-another probability calculator, and no exclusions.

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