Initial commit: Lean Experiment MCP server v1.0.0
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README.md
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# Lean Experiment MCP Server
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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.
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## Why This Exists
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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.
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## Tools
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### `define_experiment`
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Create a new experiment with hypothesis, success criteria, and kill criteria.
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```json
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{
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"name": "MCP Server Adoption Test",
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"hypothesis": "If I publish a free MCP server, 50+ agents will install it in 30 days because the registry has 180k users",
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"success_criteria": "50+ stars/downloads in 30 days, 5+ issues opened",
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"kill_criteria": "Zero engagement after 14 days",
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"duration_days": 30
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}
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```
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### `record_observation`
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Log a data point for an active experiment.
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```json
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{
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"experiment_id": "exp_...",
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"metric": "github_stars",
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"value": 12,
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"notes": "Posted on HN, got 3 upvotes"
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}
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```
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### `get_experiment_status`
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Get full status with metrics summary, days remaining, and observations.
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### `list_experiments`
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List all experiments with status, day count, and observation count.
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### `make_decision`
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Make a decision: **persevere**, **pivot**, or **kill**.
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```json
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{
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"experiment_id": "exp_...",
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"decision": "kill",
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"reason": "Zero installs after 14 days. Hypothesis invalidated."
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}
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```
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### `update_experiment`
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Update notes or extend duration.
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## Installation
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### Via npx (no install)
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```json
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{
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"mcpServers": {
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"lean-experiments": {
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"command": "npx",
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"args": ["lean-experiment-mcp"]
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}
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}
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}
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```
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### Via npm
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```bash
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npm install -g lean-experiment-mcp
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```
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## Data Storage
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All experiments stored in `~/.lean-experiments/experiments.db` (SQLite).
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## Example Workflow
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1. **Define:** `define_experiment` with hypothesis and success/kill criteria
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2. **Ship:** Build the thing, publish it
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3. **Record:** Every few days, log metrics with `record_observation`
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4. **Decide:** At the end, use `make_decision` to persevere, pivot, or kill
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5. **Learn:** Review all experiments with `list_experiments`
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## License
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MIT
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