Archive an experiment by ending it and returning the updated experiment. Historical exposures, conversion events, and reports remain available.
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | No | Optional project ID. Omit only when the API key is project-scoped or the account has a clear default project. | |
| experimentId | Yes | Experiment ID returned by experiments_create or experiments_list. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the experiment is ended and returns the updated experiment, and that historical data persists, adding context beyond the destructiveHint annotation. However, it does not specify permissions or reversibility.
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?
Two sentences with no wasted words; front-loaded with the main action and return value.
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?
Given two simple parameters and no output schema, the description adequately covers the operation, though the return value structure could be elaborated.
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 coverage is 100%, so the description adds no new parameter information; 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 clearly states the action ('Archive an experiment by ending it') and the resource ('experiment'), and clarifies that historical data remains available, distinguishing it from other experiment tools.
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 usage for finalizing an experiment while preserving data, but does not explicitly contrast with alternatives like experiments_set_status or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action combination (e.g., domains_add, experiments_create, goals_deactivate, reports_experiment_chart). There is no overlap or ambiguity; even the three report tools serve clearly different purposes (totals, time-series, channel breakdown).
The vast majority of tools follow a resource_action snake_case pattern (domains_add, experiments_list). A couple deviate (billing_portal, usage_summary) but still place the resource first, making the pattern predictable and easy to parse.
With 23 tools covering projects, domains, experiments, goals, reports, billing, health, and usage, the count is well-scoped for a split-testing platform. Each tool addresses a specific need without ballooning into excessive granularity.
The tool surface provides CRUD-like operations for core entities (projects, experiments, goals, domains) and essential report types. Minor gaps exist (no goal update tool, no experiment delete—only archive) but these are reasonable trade-offs for the domain.
