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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds meaningful context about what the tool returns — actor identity, originating AI app, changed fields, and timestamps — beyond the annotations, though it does not discuss pagination or ordering.

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?

It is a single, front-loaded sentence with no filler. The key information (scope, return contents) comes immediately, and every phrase earns its place.

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 read-only single-parameter audit tool with no output schema, the description adequately explains what the return contains (who, what, when, and via which app). It stops short of mentioning result ordering, pagination, or time-range limits, which would fully close the gap.

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?

There is a single required parameter with 100% schema description coverage ('The class's id'), so the schema already fully documents it. The description implies the id selects one class but adds no further syntax, format, or validation detail beyond the schema. Baseline 3 applies when schema coverage is high.

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 states a specific resource and scope: 'Every change to one class,' and specifies the return payload (who, what, when, and which AI app). It clearly distinguishes this audit-history tool from siblings like get_class or edit_classes.

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 audit-oriented wording ('every change'), but there is no explicit when-to-use guidance, no exclusion, and no named alternative such as get_class for current state. An agent can infer the purpose but is not explicitly routed.

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