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product-on-purpose

PM-Skills MCP

pm_workflow_lean_startup

Validate product hypotheses using the Build-Measure-Learn cycle. This workflow guides you through hypothesis creation, experiment design, instrumentation, results analysis, and pivot decisions.

Instructions

Lean Startup Validation workflow - Build-Measure-Learn cycle for validating product hypotheses through experimentation.

Effort Level: comprehensive

Steps:

  1. pm_hypothesis

  2. pm_experiment_design

  3. pm_instrumentation_spec (optional)

  4. pm_experiment_results

  5. pm_pivot_decision

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
Behavior3/5

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

No annotations provided, so description carries the burden. It states it returns a Markdown plan without side effects. While adequate, it could be more explicit about being a non-destructive read-only plan generation.

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

Conciseness4/5

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

Well-structured with clear sections: purpose, effort level, steps, args, returns. Front-loaded with purpose. Could be slightly more concise but no wasted sentences.

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 workflow plan generator with 2 parameters and no output schema, the description adequately explains the output (Markdown plan) and the steps involved. Complete enough for an agent to use appropriately.

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 3 applies. Description adds minimal new meaning beyond the schema: 'subject or feature' for topic and 'additional context' for context. No extra detail on format or constraints.

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 names the workflow as 'Lean Startup Validation' and explains it as a 'Build-Measure-Learn cycle', providing specific steps. This distinguishes it from other workflow siblings 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 Guidelines4/5

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

Explicitly states 'Use this tool to get a complete workflow plan' and describes orchestration by calling step tools in sequence. Lacks explicit when-not-to-use but provides sufficient context for appropriate use.

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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