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Create Experiment

create_experiment

Creates a Build-Measure-Learn experiment record to track a product hypothesis. Accepts title, state, metric, target, and full hypothesis.

Instructions

Create a PM experiment (a Build-Measure-Learn hypothesis) and return it. state is 'hypothesis' (default) | 'build' | 'measure' | 'learn'. Only title is required. This is the PM tracker list_experiments reads, not the analytics A/B engine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoBuild-Measure-Learn stage (optional; default 'hypothesis').
titleYesExperiment title / the hypothesis in a line (required).
metricNoThe metric it moves, e.g. 'activation rate' (optional).
targetNoTarget change, e.g. '+5pp' (optional).
hypothesisNoThe full hypothesis (optional).
product_idNoProduct, from whoami (optional).
Behavior3/5

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

Description adds return behavior and state enumeration beyond annotations, but does not mention side effects, permissions, or limitations. Annotations already indicate non-readOnly, so the added value is moderate.

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, each earning its place: first defines action and object, second clarifies state values, required field, and context against sibling tool. No wasted words.

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?

Covers creation, required fields, state options, and differentiates from analytics engine. Does not describe return structure or relationships, but is fairly complete given no output schema and 6 parameters.

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 coverage is 100% so baseline is 3. Description reinforces that only title is required and explains state enum, but does not add significant new meaning beyond schema descriptions.

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?

Clearly states it creates a PM experiment (a Build-Measure-Learn hypothesis) and returns it. Distinguishes from the analytics A/B engine via explicit mention.

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

Usage Guidelines4/5

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

Clearly implies use for PM experiment creation and distinguishes from A/B testing tool. Does not explicitly specify when to use vs other Create tools, but 'only title is required' gives helpful usage guidance.

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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