Return billing usage such as sessions, events, exposures, and active experiments for the selected project.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | Optional YYYY-MM-DD usage end date. | |
| from | No | Optional YYYY-MM-DD usage start date. | |
| 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 indicate read-only, idempotent, non-destructive. Description adds specific data types returned, which is helpful context beyond 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?
Single sentence, under 20 words, no redundancy. Efficiently conveys the tool's purpose.
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?
Covers the core return type with examples. Could be improved by mentioning default behavior for missing dates, but adequate for a simple read tool.
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?
100% schema coverage means baseline is 3. Description adds little extra meaning beyond the schema descriptions for 'to', 'from', and '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?
Clearly states the tool returns billing usage (sessions, events, etc.) for a selected project, differentiating it from siblings like experiments or reports.
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?
Implied usage for retrieving billing usage, but no explicit when-to-use or when-not-to-use guidance, nor comparison to siblings.
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.
