Retrieve A/B test experiment results, including conversion rates per treatment, to compare variant performance and identify which treatment drives better outcomes.
Get results for an A/B test experiment including conversion rates per treatment
| Name | Required | Description | Default |
|---|---|---|---|
| experimentId | Yes | The experiment ID |
Changes observed during successful MCP inspections.
v0.2.1Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Input schema / additionalPropertiesRemoved value: -falsev0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context that results include conversion rates per treatment, but it does not describe freshness, finality, or access requirements for the results.
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 with the core action and returned information front-loaded. There is no redundant or wasted language.
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 read-only tool with one fully documented parameter and annotations covering safety behavior, the description is largely complete. It could further clarify result freshness or whether only completed experiments return data, but no output schema exists to require return-value explanation.
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 schema has 100% description coverage for the single experimentId parameter, so the baseline is 3. The description does not add meaning beyond the schema, such as the expected ID format or whether experiments must be in a particular state.
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 verb and resource: getting results for an A/B test experiment. It also clarifies the returned content as conversion rates per treatment. It does not explicitly distinguish itself from sibling tools like get_experiment or list_experiments, so it stops short of a 5.
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 implies that this tool is used when experiment results are needed, but it gives no explicit guidance on when to use it instead of get_experiment, list_experiments, or other experiment-related tools. No exclusions or alternatives are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.