get_experiment_results

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the output includes conversion rates but does not explicitly confirm the operation is read-only, idempotent, or require specific permissions. Preconditions (e.g., experiment must exist or be completed) and error behavior are omitted.

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 description is a single, focused sentence that front-loads the action and includes the most valuable detail (conversion rates per treatment). There is no fluff, and every word contributes to understanding the tool's core function.

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 tool with one well-documented parameter, the description is minimally viable but incomplete. It lacks details about the full return structure (beyond conversion rates), whether the experiment must be stopped/running, and any caveats about result interpretation. The absence of an output schema makes this gap more significant, but the tool's simplicity keeps it from being critically inadequate.

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 input schema fully describes the single parameter (experimentId) with 100% coverage. The description does not add additional parameter-level context, but given the schema already provides the meaning, the baseline score of 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 is specific and action-oriented: 'Get results for an A/B test experiment including conversion rates per treatment.' It clearly identifies the resource (experiment results) and key data returned, distinguishing it from tools like get_experiment which likely fetch configuration details.

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?

No guidance is provided on when to use this tool versus alternatives like get_experiment or list_experiments. The description does not mention exclusions, prerequisites, or scenarios where another tool would be more appropriate, leaving the agent to infer usage from the name alone.

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