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

The description goes beyond the readOnlyHint annotation by explaining that it returns the posterior probability and a per-step chain, and that it can process multiple evidence items. The word 'update' refers to a mathematical calculation rather than a persistent state change, so there is no contradiction with the readOnly annotation.

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?

Three sentences cover the main operation, input/output mechanics, and usage triggers without redundancy. The description is front-loaded with the core Bayesian update purpose and each subsequent sentence adds a distinct, useful layer.

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 stateless calculator with a readOnlyHint and fully documented schema, the description gives enough to invoke correctly: what to pass, how evidence is modeled, and what comes back. It could detail the structure of the per-step chain, but that is secondary to calling the tool successfully.

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%, and the input schema already fully documents prior formats, evidence structure, and likelihood semantics. The description repeats the evidence model at a high level but adds no parameter-level meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 action ('update a prior probability') with a precise mathematical method (Bayes theorem) and clearly describes the input/output relationship. The 'Use for' phrases ('posterior probability', 'how does this news change the odds') make it easy to distinguish from the conversion and EV tools in the sibling list.

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?

Explicitly provides user-intent triggers such as 'update my estimate with new information' and 'posterior probability', which helps an agent decide when to select this tool. It does not name specific sibling alternatives or state when not to use it, but the context is clear enough.

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