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

Goes well beyond the annotations by disclosing concrete effects: resets the rule's staleness clock, restores confidence to the configured maximum, and removes the rule from the review queue. These side effects are the key behavioral traits an agent needs and are not derivable from readOnlyHint/idempotentHint alone.

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?

Front-loads the core action in the first sentence, then the rationale and effects, ending with a tight Args section. Every sentence carries information; nothing is redundant padding.

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?

With no output schema and only one required parameter, the description supplies everything needed to call the tool correctly: what it does, when to use it, what changes as a result, and the parameter's meaning.

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 0% and the schema only types rule_id as a string, but the description compensates by specifying it is the UUID of the verified rule, adding format/identity meaning. With a single parameter this is sufficient, though no extra validation or lookup guidance is given.

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 (affirm/record) and resource (rule), and precisely defines the act as verifying a rule against current state. It is clearly distinguishable from siblings like create_rule, list_rules, and evaluate_hypothesis, which an agent can tell apart without opening the schema.

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 states the when-to-use condition ('rules actually verified as correct') and the anti-condition ('not for rules that were merely retrieved or look plausible'). It also frames the motivation: rules decay and low-confidence rules get queued for review.

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