List experiments for a SplitLaunch project, optionally filtered by lifecycle status. Results are paginated and ordered newest first.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Default: 1. | |
| status | No | Optional lifecycle status filter. | |
| perPage | No | Default: 25. Maximum: 100. | |
| projectId | No | Optional project ID. Omit only when the API key is project-scoped or the account has a clear default project. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds valuable behavioral context: pagination and ordering 'newest first', which are not in the schema or annotations. It does not contradict any 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 description is a single, well-structured sentence that front-loads the action and includes essential details (scoping, filtering, pagination, ordering). No unnecessary words.
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 4 parameters (all documented in schema) and no output schema, the description provides a clear overview of the tool's behavior. It lacks explicit mention of return format, but the purpose and key behaviors are sufficiently covered for an AI agent to use correctly.
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 100%, so baseline is 3. The description mentions 'optionally filtered by lifecycle status', which is already in the schema, and does not add significant meaning beyond the schema for other parameters like page, perPage, or projectId.
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 verb 'List' and the resource 'experiments for a SplitLaunch project', with optional filtering and pagination details. It effectively distinguishes from sibling tools like experiments_get (singular) and experiments_archive (modification).
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 when to use (list experiments) and mentions optional filtering. While it does not explicitly state when not to use or list alternatives, the context of sibling tools makes the usage clear. The lack of explicit exclusions or prerequisites is acceptable given the low complexity.
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.
