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

Goes well beyond the annotations by disclosing quota cost (one AI text action from the plan), the replacement of existing model state, the failure mode with no published posts, and the need for user confirmation. destructiveHint=true already flags mutation, but the description adds concrete operational consequences.

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?

Four short sentences, each carrying distinct information (purpose, replacement effect, cost/precondition, confirmation requirement) with the core action front-loaded. No filler or repetition.

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 parameterless mutation with no output schema, the description covers everything an agent needs: what it does, what it overwrites, the quota cost, the failure condition, and the required user confirmation step.

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?

The tool takes zero parameters, so there are no parameter semantics to explain; the baseline for a parameterless tool is 4. Nothing in the description is needed to compensate for schema gaps.

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 ('train/retrain') and resource ('the user's own voice model'), plus the data source (last 50 published posts). It is easily distinguished from the sibling get_voice_model (read) versus this write/retrain operation.

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?

Gives clear conditions: use when training or retraining, and requires user confirmation before retraining because it replaces the existing model. It also states the precondition under which it fails (no published posts). It does not explicitly name alternative tools, but the when-to-use context is unambiguous.

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