SplitLaunch

reports_experiment_details

Read-onlyIdempotent

Return experiment totals by variant: exposures, visitors, conversions, revenue when available, and conversion rate. Revenue is zero when the plan does not include revenue tracking.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	Optional ISO-8601 end date/time or YYYY-MM-DD date.
`from`	No	Optional ISO-8601 start date/time or YYYY-MM-DD date.
`goal`	No	Optional conversion event name to filter report results.
`projectId`	No	Optional project ID. Omit only when the API key is project-scoped or the account has a clear default project.
`experimentId`	Yes

TDQS

A4/5.0

Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds that revenue may be zero based on plan, which is useful behavioral context beyond annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no waste. Front-loaded with main purpose and outputs. Efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description explains the key output fields and a special case (revenue). It could mention date range behavior or goal filtering, but overall it provides sufficient context for a summary report tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is high (80%), so description adds no additional parameter details. It explains the output but not the parameters themselves, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool returns experiment totals by variant, listing specific fields (exposures, visitors, conversions, revenue, conversion rate). It distinguishes from sibling tools like reports_experiment_channels and reports_experiment_chart which have different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description mentions a condition (revenue zero when plan lacks tracking) but does not explicitly guide when to use this tool vs siblings like reports_experiment_channels or reports_experiment_chart. Usage context is implied but not stated.

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

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TDQS

A3.9/5.0

Disambiguation5/5

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).

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources

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