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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false. The description adds useful return-content context, namely that variants and full Bayesian results are included, which is not in the annotations. It does not cover not-found behavior or other operational details, but the bar is lower with annotations.

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?

The definition is a single front-loaded fragment with no filler. It is appropriately sized for a simple one-parameter lookup, though its terseness is also the source of the usage-guidance gap.

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 simple one-parameter read tool with no output schema, the description gives enough return-value orientation ('variants and full Bayesian results') and the annotations cover safety. It is not fully complete because it omits failure behavior and explicit when-to-use guidance.

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 single parameter has 100% schema coverage and is documented as 'Experiment id, from list_experiments'. The description adds no additional parameter semantics beyond the schema baseline, so 3 is appropriate.

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 identifies the resource and singular scope ('One experiment') and states what it returns ('variants and full Bayesian results'). It is clear that this is the single-experiment counterpart to list_experiments, though it does not explicitly name the sibling or use a verb.

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 gives no explicit instruction on when to use this tool versus list_experiments or other experiment tools. The only hint is the id parameter's schema note 'from list_experiments', leaving the retrieve-by-id use case to be inferred.

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