MCP server for running Lean Startup experiments — define hypotheses, track observations, make data-driven decisions
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Lean Experiment MCP Server

A Model Context Protocol (MCP) server for running Lean Startup experiments. Define hypotheses, track observations, and make data-driven decisions — all from within your agent workflow.

Why This Exists

Agents need to validate ideas before building. This MCP server gives agents the same Lean Startup tooling that human startups use: hypothesis definition, metric tracking, and explicit persevere/pivot/kill decisions.

Tools

define_experiment

Create a new experiment with hypothesis, success criteria, and kill criteria.

{
  "name": "MCP Server Adoption Test",
  "hypothesis": "If I publish a free MCP server, 50+ agents will install it in 30 days because the registry has 180k users",
  "success_criteria": "50+ stars/downloads in 30 days, 5+ issues opened",
  "kill_criteria": "Zero engagement after 14 days",
  "duration_days": 30
}

record_observation

Log a data point for an active experiment.

{
  "experiment_id": "exp_...",
  "metric": "github_stars",
  "value": 12,
  "notes": "Posted on HN, got 3 upvotes"
}

get_experiment_status

Get full status with metrics summary, days remaining, and observations.

list_experiments

List all experiments with status, day count, and observation count.

make_decision

Make a decision: persevere, pivot, or kill.

{
  "experiment_id": "exp_...",
  "decision": "kill",
  "reason": "Zero installs after 14 days. Hypothesis invalidated."
}

update_experiment

Update notes or extend duration.

Installation

Via npx (no install)

{
  "mcpServers": {
    "lean-experiments": {
      "command": "npx",
      "args": ["lean-experiment-mcp"]
    }
  }
}

Via npm

npm install -g lean-experiment-mcp

Data Storage

All experiments stored in ~/.lean-experiments/experiments.db (SQLite).

Example Workflow

  1. Define: define_experiment with hypothesis and success/kill criteria
  2. Ship: Build the thing, publish it
  3. Record: Every few days, log metrics with record_observation
  4. Decide: At the end, use make_decision to persevere, pivot, or kill
  5. Learn: Review all experiments with list_experiments

License

MIT