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

Annotations already declare the full safety profile (readOnly, idempotent, non-destructive, non-open-world), so the bar is low. The description nonetheless adds real behavioral information beyond them: priors are renormalised to sum to 1 and the return is normalised posteriors, which matters given there is no output schema. It omits degenerate-case behaviour (e.g. all-zero likelihoods/prior mass).

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 tight sentences with zero filler; the core operation and the normalisation rule are front-loaded before any detail.

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 single-parameter tool with no output schema, the description covers the input's meaning, the computation, and the returned normalised posteriors. Missing only edge-case/degenerate-input handling, which is a minor gap at this complexity level.

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 0%, so the description must carry parameter meaning. It does explain the two required nested fields (prior, likelihood of observed evidence) and clarifies that priors need not sum to 1, but it ignores the optional 'name' field and gives no numeric constraints or accepted ranges.

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 ('Discrete Bayesian update') and resource ('competing hypotheses each with a prior and likelihood'), and names the output ('normalised posteriors'). This is clearly distinct from the numeric siblings (descriptive_stats, linear_regression), though it never explicitly contrasts itself with hypothesis_test, which is the nearest neighbour.

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 implies the applicable situation ('given competing hypotheses each with a prior and the likelihood of the observed evidence'), so usage is inferable. However, it gives no explicit when-to-use/when-not guidance and never points to an alternative sibling for related inferential questions.

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