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

Annotations already declare readOnlyHint=false, destructiveHint=false, idempotentHint=false and openWorldHint=false, so the safety profile is covered. The description adds real value by declaring the `write` scope/auth requirement and the POST endpoint, plus the fact that created content starts as drafts requiring a separate publish. It does not disclose error behavior, duplicate-name handling, or what the call returns, so it stays at 3 rather than higher.

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?

Three sentences, front-loaded with the scope marker and the action, then the workflow. Every clause carries information (auth scope, endpoint, labeling convention, next steps) with no filler.

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 10-parameter mutation tool with no output schema and near-zero schema description coverage, the definition is thin: it never explains what the call returns (the experiment id/label the agent must reuse), nor the meaning of status, primaryMetric, or variant ordering. The auth scope and workflow are covered, which is more than most, but parameter-level completeness is lacking.

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 only 10% across 10 parameters, so the description has to carry the load and largely does not. It only clarifies `compareKey` and `variants` indirectly through the `<compareKey>:<variant>` label convention and hints that the response yields a `label`. `brandId`, `status`, `primaryMetric`, `startAt`/`endAt`, `hypothesis` and `notes` are left entirely to the schema. The partial explanation of the variant/label relationship is worth some credit but leaves most parameters undocumented.

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 and resource ("Creates an A/B experiment") and even names the underlying endpoint, so an agent can distinguish it from update_experiment or create_content. It stops short of explicitly contrasting with the other experiment siblings (update_experiment, start_experiment_round), but the A/B framing is unambiguous.

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 a concrete follow-on workflow: tag drafts with the experiment `label` and `<compareKey>:<variant>`, publish, then read get_experiment_results. That tells the agent when this tool is the entry point in a sequence. It does not state when not to use it (e.g. versus update_experiment for an existing experiment), which keeps it from a 5.

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