bayes_update_hypotheses

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

With no annotations, the description only says it normalizes data into posteriors but does not disclose validation behavior, output format, or handling of invalid inputs.

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?

Single sentence with zero wasted words, efficiently conveying the core function.

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?

Missing output schema and description does not explain return format, error handling, or edge cases, leaving the tool incomplete for an agent.

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%, and the description adds minimal context beyond schema, not explaining constraints like priors summing to 1 or meaning of likelihood.

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 clearly states the tool normalizes priors and likelihoods into posterior probabilities for mutually exclusive, exhaustive hypotheses, specifying the verb, resource, and conditions.

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 usage only for mutually exclusive, exhaustive hypotheses but does not explicitly compare with sibling like bayes_theorem or provide when-not-to-use guidance.

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