Metrics question
Suppose Anthropic wants to explore a new developer product adjacent to Claude Code or MCP. What is the cheapest MVP you would build in 4-6 weeks, which parts would you prototype yourself versus hand to engineering, and what early signals would convince you the idea has real pull rather than novelty?
- Anthropic
- Metrics
- Hard
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What this question tests
Tests scrappy MVP scoping for a developer product and defining leading indicators of real pull versus novelty within a tight timeline.
How to approach it
- Pick a concrete adjacent opportunity, such as a tool for managing MCP server permissions or a workflow-specific Claude Code extension.
- Scope the cheapest possible version, such as a CLI wrapper, rather than a polished integrated feature.
- Decide what to prototype yourself, like the core workflow using existing APIs, versus hand to engineering, like new infrastructure.
- Get it in front of a small group of real developers within the window, not just internal dogfooding.
- Define early pull signals: unprompted repeat usage or specific feature requests, versus merely polite positive feedback.
What a strong answer includes
- Scopes the MVP to reuse existing primitives, like the MCP protocol itself, rather than building new infrastructure, keeping it genuinely cheap.
- Names a concrete signal of real pull, such as a developer using it unprompted in week two without being reminded.
- Explicitly distinguishes engineers building it because it's technically interesting from developers asking for more of it.
- Sets a kill criterion, such as no organic repeat usage by the deadline means stop.
Common mistakes
- Building a fuller version than the MVP allows because it's more impressive to demo internally.
- Mistaking internal engineering enthusiasm for external developer pull.
Likely follow-up questions
- Who specifically would you recruit to test this in week one?
- What would make you extend this past six weeks versus killing it?
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More questions from Anthropic
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop