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AIOProductOS

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by AIOProductOS
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JavaScript
Remote

Create Experiment

create_experiment

Create a product experiment to track Build-Measure-Learn stages, including title, metric, target, and hypothesis. Manage experiments from idea to learning.

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.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "title": "Shorter onboarding lifts activation",
      "metric": "activation rate",
      "target": "+5pp"
    },
    "name": "create_experiment"
  }
}

Input 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).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.14
  2. Removedv0.1.12
  3. Addedv0.1.6

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate non-readonly, non-idempotent, non-destructive behavior. The description adds that the tool returns the created experiment, defaults state to 'hypothesis', and clarifies the storage/read mechanism via list_experiments. This provides useful behavioral context beyond the annotations, though it does not elaborate on error cases or auth requirements.

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?

The description is concise and front-loaded with the core purpose. It packs the essential info—purpose, return, required field, state default, and differentiation from A/B engine—into four short sentences with no fluff or verbosity.

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?

For a create tool with no output schema, the description covers the key aspects: what it creates, return behavior, required/optional fields, state default, and where it fits in the broader tool ecosystem. It does not describe the return structure in detail or mention prerequisites like authentication, but annotations and schema fill in some gaps, making it fairly complete.

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 100%, with each parameter already well-documented (state enum, required title, optional metric, target, hypothesis, product_id). The description only repeats the state default and required title, adding no new meaning beyond the schema. Baseline 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?

The description clearly states the tool creates a PM experiment (a Build-Measure-Learn hypothesis) and returns it. It explicitly distinguishes this from the analytics A/B engine, which sets it apart from related tools and potential confusion.

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?

The description gives clear context: this is the PM tracker that list_experiments reads, and explicitly states it is not the analytics A/B engine. It also says only title is required, providing a key usage constraint. However, it does not name an explicit alternative tool for A/B experiments, slight gap for full when-to-use 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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