Case Study 5: Dev Tool MVP

I built a CLI tool intended to standardize local development setup across microservices. The promise: one command—dev bootstrap—that discovers services, generates .env files, and starts containers via Docker Compose. In demos, it was magical. In real teams, it broke in 40% of setups due to bespoke scripts, Compose version drift, OS differences, and odd edge cases. The MVP automated too much, too early, and eroded trust.

This article explains what I built, why it failed, and how I would rebuild the MVP around a clear compatibility contract and a validator-first workflow that earns trust before automating.

The Context: Diverse Stacks, Fragile Automation

Microservice repos evolve organically. Teams glue together language-specific tools, local caches, custom scripts, and different container setups. A tool that tries to own the entire “bootstrap and run” flow without a shared contract is brittle.

What I Built (MVP Scope)

• Discovery: Scan repos for services via file patterns.
• Env Generation: Infer env keys from docker-compose.yml and sample .env.example files; produce unified .env.
• Compose Orchestration: Start all services locally with one command.
• Opinionated Defaults: Assume standard port ranges and common service names.
• Metrics: Time to first run, number of successful bootstraps per team.

Launch and Early Results

• Solo demos worked spectacularly.
• Team pilots revealed fragility: custom scripts, non-standard Compose naming, and OS-specific quirks caused frequent failures.
• Trust dropped quickly; teams reverted to their known scripts.

Why It Failed: Over-Automation Without a Contract

I tried to automate the whole workflow without agreeing on a small, stable contract that teams could satisfy. Without a shared “dev.json” or similar spec, guessing env keys and start commands led to errors. Reliability suffered, and with dev tools, reliability is the MVP.

Root causes:

• Inference Errors: Guessing configurations from heterogeneous repos is error-prone.
• Hidden Assumptions: Opinionated defaults clashed with local reality.
• No Validation Step: Users couldn’t see or fix mismatches before automation ran.

The MVP I Should Have Built: Validate and Guide

Start with a minimal compatibility contract and a validator that helps teams conform incrementally.

• Contract: Each service exposes a dev.json containing ports, env keys, and start command.
• Validator CLI: dev validate checks conformance, explains gaps, and suggests fixes.
• Linter: Provide a linter for dev.json with clear error messages.
• Guided Setup: Generate .env from dev.json and start one service at a time.
• Telemetry: Track validation pass rate, categories of errors, and time to first successful run.

How It Would Work (Still MVP)

• Step 1: Teams add dev.json to each service with minimal fields.
• Step 2: Run dev validate; fix issues based on actionable messages.
• Step 3: Use dev env to generate environment files deterministically.
• Step 4: Start one service with dev run service-a; expand to orchestration only after a high pass rate.

This builds trust by making the tool predictable and by exposing mismatches early.

Technical Shape

• Schema: dev.json with fields { name, port, env: [KEY], start: "cmd" }.
• Validation Engine: JSON schema + custom checks (port conflicts, missing env keys).
• Compose Adapter: Optional; reads from dev.json to generate Compose fragments rather than infer from arbitrary files.
• Cross-Platform Tests: Simple checks for OS differences (path separators, shell commands).

Measuring Trust

• Validation Pass Rate: Percentage of services passing dev validate.
• First Successful Run: Time from install to one service running.
• Error Categories: Distribution helps prioritize adapters and docs.
• Rollback Incidents: Track how often teams abandon the tool mid-setup.

Onboarding and Documentation

• Quick Start: Create dev.json with a template; run dev validate.
• Troubleshooting: Clear guides for common errors with copy-paste fixes.
• Contracts Over Recipes: Emphasize the compatibility contract and why it exists.

Personal Reflections

I wanted the “it just works” moment so much that I skipped the steps that make “it just works” possible: a shared spec and a validator. Dev teams reward predictability over magic; trust is the currency.

Counterfactual Outcomes

With a validator-first MVP:

• Validation pass rate climbs from ~40% to ~80% in two months.
• Time to first successful run drops significantly.
• Teams adopt the tool gradually, and orchestration becomes feasible.

Iteration Path

• Add adapters for common stacks (Node, Python, Go).
• Introduce a dev doctor command that diagnoses OS and toolchain issues.
• Expand the contract only as needed; resist auto-inference beyond the spec.

Closing Thought

For dev tools, the smallest viable product is a trust-building tool: define a minimal contract, validate it, and guide teams to conformance. Automate only after reliability is demonstrated. Magic is delightful, but trust is what sticks.