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product-on-purpose

PM-Skills MCP

pm_workflow_experiment_cycle

Run a complete experiment workflow from hypothesis to lessons learned. Produces a step-by-step plan covering design, instrumentation, and results.

Instructions

Experiment Cycle workflow - Complete experimentation workflow from hypothesis through learning capture.

Effort Level: standard

Steps:

  1. pm_hypothesis

  2. pm_experiment_design

  3. pm_instrumentation_spec

  4. pm_experiment_results

  5. pm_lessons_log

Use this tool to get a complete workflow plan. The AI client orchestrates execution by calling each step's tool in sequence.

Args:

  • topic (string, required): The subject or feature for this workflow

  • context (string, optional): Additional context for the workflow

Returns: Markdown workflow plan with steps, guidance, and execution instructions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesThe subject or feature for this workflow
contextNoAdditional context, constraints, or requirements
Behavior4/5

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

The description explains the tool returns a Markdown workflow plan with steps and guidance, and outlines the steps. No annotations are provided, but the description covers the main behavior. It lacks mention of authorization or side effects, but for a plan-generating tool, this is acceptable.

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, well-structured with bullet points for steps, and front-loaded with the purpose. Every sentence adds value.

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

Completeness5/5

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

For a moderate-complexity workflow plan tool, the description covers the workflow steps, parameters, and return value. No output schema exists, but the description adequately explains what is returned. Complete given context.

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?

Parameter schema coverage is 100% and both parameters are described in the schema. The description repeats the parameter info but adds no extra semantics beyond the schema. 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?

The description clearly states it is a complete experimentation workflow from hypothesis through learning capture, and explicitly mentions use to get a workflow plan. It lists specific steps, distinguishing it from sibling workflow tools.

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 says 'Use this tool to get a complete workflow plan' and that the AI orchestrates execution, providing clear usage context. However, it does not explicitly state when not to use or compare to alternatives.

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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