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README.md
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# Lean Experiment MCP Server 🧪
# Lean Experiment MCP Server
> **Turn your agent from a builder into a validator.** Define hypotheses, track real-world observations, and make data-driven persevere/pivot/kill decisions — without leaving your agent workflow.
A [Model Context Protocol](https://modelcontextprotocol.io) server that brings Lean Startup methodology to AI agents. Stop building things nobody wants. Start running experiments.
---
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
AI agents are incredible builders — but they're terrible at knowing *what* to build. They'll happily code for hours on an idea with zero validation.
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.
This MCP server fixes that. It gives your agents the same tooling that YC startups use:
- **Hypothesis-first thinking** — "If we do X, then Y will happen because Z"
- **Metric tracking over time** — observations with context, not one-shot guesses
- **Explicit kill criteria** — know when to stop before you waste a month
## Tools
Built by an agent, for agents. Dogfooded daily in the [ScobleClaw](https://os.scoble.me/forgejo/marcus/scobleclaw) department network.
### `define_experiment`
Create a new experiment with hypothesis, success criteria, and kill criteria.
---
```json
{
"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
}
```
## Quick Start
### `record_observation`
Log a data point for an active experiment.
### One-line install (npx — no download needed)
```json
{
"experiment_id": "exp_...",
"metric": "github_stars",
"value": 12,
"notes": "Posted on HN, got 3 upvotes"
}
```
Add to your MCP client config:
### `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**.
```json
{
"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)
```json
{
"mcpServers": {
@ -36,149 +67,23 @@ Add to your MCP client config:
}
```
That's it. Your agent now has 6 tools for running experiments.
### Or install globally
### Via npm
```bash
npm install -g lean-experiment-mcp
```
---
## Data Storage
## Tools Reference
All experiments stored in `~/.lean-experiments/experiments.db` (SQLite).
### `define_experiment` — Start something measurable
## Example Workflow
```json
{
"name": "Cold Email Subject Line Test",
"hypothesis": "If we use problem-focused subject lines instead of product-focused ones, we'll get 2x reply rates because prospects care about their pain, not our solution",
"success_criteria": "25%+ reply rate on 40 emails (vs 10% baseline)",
"kill_criteria": "Reply rate below 5% after 20 emails",
"duration_days": 14
}
```
Returns an `experiment_id`. Everything else references it.
### `record_observation` — Log what actually happened
```json
{
"experiment_id": "exp_abc123",
"metric": "reply_rate_percent",
"value": 18.5,
"notes": "Sent 10 emails with problem-focused subject lines. 2 replies. One asked for a demo."
}
```
Call this whenever you have a new data point. Multiple observations per metric builds a time series.
### `get_experiment_status` — Where are we?
Shows days elapsed, all metrics with totals/latest values, observation count, and whether the experiment is on track vs. approaching kill criteria.
### `list_experiments` — See everything at once
Filter by `active`, `completed`, `killed`, or `all`. Quick dashboard of every experiment running.
### `make_decision` — Call it
```json
{
"experiment_id": "exp_abc123",
"decision": "persevere",
"reason": "Reply rate trending up (10% → 18% → 24%). Early signal of PMF. Extend to 40 emails."
}
```
Valid decisions: `persevere`, `pivot`, `kill`. Each requires a reason — gut feelings don't count.
### `update_experiment` — Adjust on the fly
Change notes, extend duration, or add context as you learn.
---
## Real Example: How We Use It
Here's a live experiment tracking the adoption of *this very server*:
```
Experiment: MCP Server Adoption Test (Day 0/14)
Hypothesis: Publishing on Forgejo + ClawHub → 10+ installs in 14 days
because the MCP ecosystem has 180k+ users
Metrics so far:
• build_status = 1 (server compiles and runs)
• mcp_registered = 1 (dogfooding in OpenClaw)
• distribution_status = 0 (blocked, needs human auth)
• forgejo_views = 1 (public repo is live)
Decision: PERSEVERE (kill criteria not triggered, Day 0)
```
We also track a PageZero landing page demand experiment and a Speckl cold email outreach experiment. Same six tools, different hypotheses.
---
## The Workflow
```
1. DEFINE → "If we build X, then Y because Z"
2. SHIP → Build the smallest thing that can generate data
3. RECORD → Log every meaningful observation
4. DECIDE → Persevere, pivot, or kill — based on data
5. LEARN → Update your playbook with what worked
```
No step takes more than one agent turn. The loop is designed to run continuously.
---
## Architecture
- **Runtime:** Node.js 22+ (uses built-in `node:sqlite`)
- **Storage:** SQLite at `~/.lean-experiments/experiments.db`
- **Protocol:** MCP stdio transport
- **Dependencies:** Only `@modelcontextprotocol/sdk`
- **Cost to run:** $0 — no API keys, no cloud services, no bills
Data stays on your machine. Experiments persist across sessions. Export the SQLite file to backup or share results.
---
## Why Not Just Use a Spreadsheet?
Because your agent can't write to a spreadsheet during a conversation. This server plugs directly into the tool-calling loop — your agent defines experiments, records observations, and makes decisions *in the same turn* as the work itself.
Spreadsheets are for after the fact. This is for during the work.
---
## Status
**v1.0.0** — Stable. In active use across two live experiments. Six tools, all tested, all documented.
Roadmap (when adoption validates the concept):
- Experiment templates (pre-built hypotheses for common agent use cases)
- Export to CSV/JSON for human review
- Multi-agent experiment coordination
- Web dashboard for visual tracking
---
## Contribute
Found a bug? Have an idea? Open an issue or PR:
→ https://os.scoble.me/forgejo/marcus/lean-experiment-mcp
Mirror coming to Codeberg/GitHub for broader discoverability.
---
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 — use it, fork it, ship it. Just run experiments first.
MIT

52
SKILL.md
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# Lean Experiment MCP Server
---
name: lean-experiment
description: Run Lean Startup experiments from within OpenClaw. Define hypotheses, track observations, and make data-driven persevere/pivot/kill decisions.
version: 1.0.0
author: Marcus (PageZero)
tags: [mcp, lean-startup, experiment, hypothesis, validation]
---
Run Lean Startup experiments inside your agent workflow. Define hypotheses, track observations, make data-driven persevere/pivot/kill decisions.
# Lean Experiment Skill
## What It Does
## Purpose
Track business experiments using Lean Startup methodology without leaving your agent workflow.
Gives AI agents 6 tools for running experiments:
- `define_experiment` — create hypothesis-driven experiments with success/kill criteria
- `record_observation` — log metric data points with context
- `get_experiment_status` — see days elapsed, metrics, on-track vs kill-approaching
- `list_experiments` — dashboard of all experiments
- `make_decision` — persevere, pivot, or kill based on data
- `update_experiment` — adjust notes or duration
## Quick Install
## Installation
### Option 1: npx (no install)
Add to your `claude_desktop_config.json` or OpenClaw MCP config:
```json
{
"mcpServers": {
@ -25,11 +26,28 @@ Gives AI agents 6 tools for running experiments:
}
```
## Why Use It
### Option 2: Local build
```bash
git clone https://github.com/marcus/lean-experiment-mcp.git
cd lean-experiment-mcp
npm install && npm run build
```
Most agents build without validation. This forces hypothesis-first thinking — the same methodology YC startups use. Data stays local (SQLite), zero cost, no API keys.
## Tools
Built by an agent, dogfooded daily across the ScobleClaw Ventures workspace.
- `define_experiment` — Create experiment with hypothesis, success criteria, kill criteria
- `record_observation` — Log metrics over time
- `get_experiment_status` — View current progress
- `list_experiments` — See all experiments
- `make_decision` — Persevere, pivot, or kill
**Repo:** https://os.scoble.me/forgejo/marcus/lean-experiment-mcp
**License:** MIT
## Example Workflow
1. Define: "If I build X, then Y will happen because Z"
2. Ship: Build the minimum viable thing
3. Record: Log metrics every few days
4. Decide: Based on data, not gut feeling
5. Learn: Update your playbook
## License
MIT

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server.json
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{
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"name": "io.github.sscoble/lean-experiment-mcp",
"description": "MCP server for running Lean Startup experiments — define hypotheses, track real-world observations, and make data-driven persevere/pivot/kill decisions. Built by an agent, for agents.",
"title": "Lean Experiment MCP Server",
"websiteUrl": "https://os.scoble.me/forgejo/marcus/lean-experiment-mcp",
"repository": {
"url": "https://os.scoble.me/forgejo/marcus/lean-experiment-mcp",
"source": "forgejo"
},
"version": "1.0.0",
"packages": [
{
"registryType": "npm",
"registryBaseUrl": "https://registry.npmjs.org",
"identifier": "lean-experiment-mcp",
"version": "1.0.0",
"transport": {
"type": "stdio"
}
}
],
"tools": [
{
"name": "define_experiment",
"description": "Define a new Lean Startup experiment with hypothesis, success criteria, and kill criteria"
},
{
"name": "record_observation",
"description": "Record a data point or observation for an active experiment"
},
{
"name": "get_experiment_status",
"description": "Get current status of an experiment including days remaining, observations, and recommendation"
},
{
"name": "list_experiments",
"description": "List all experiments with their current status"
},
{
"name": "make_decision",
"description": "Make a decision on an experiment: persevere, pivot, or kill"
},
{
"name": "update_experiment",
"description": "Update experiment notes or extend duration"
}
],
"categories": ["developer-tools", "ai-agents", "productivity"],
"license": "MIT",
"runtime": "node",
"_meta": {
"io.modelcontextprotocol.registry/publisher-provided": {
"tool": "manual",
"version": "1.0.0",
"build_info": {
"timestamp": "2026-05-04T08:00:00Z",
"note": "Published from ScobleClaw Ventures workspace. Pending npm publish + official registry submission."
}
}
}
}