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Launch Engine MCP Server

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Agentic pipeline — 39 tools from idea to revenue for solo founders.

About

Agentic pipeline — 39 tools from idea to revenue for solo founders.

Security Report

9.7
Low Risk9.7Low Risk

Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. ⚠️ Package registry links to a different repository than scanned source. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.

4 files analyzed · 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

What You'll Need

Set these up before or after installing:

Path to your project directory where pipeline-state.json and assets/ liveOptional

Environment variable: LAUNCH_ENGINE_PROJECT_DIR

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-zionhopkins-launch-engine": {
      "env": {
        "LAUNCH_ENGINE_PROJECT_DIR": "your-launch-engine-project-dir-here"
      },
      "args": [
        "-y",
        "launch-engine-mcp"
      ],
      "command": "npx"
    }
  }
}

Getting Started

Once installed, try these example prompts and explore these capabilities:

  • 1"Scout the market for [your idea]"
  • 2Three entry points available:
  • 3— scout: Full pipeline from idea to validated revenue
  • 4— rapid_test: Quick $50-100 validation in 3-5 days
  • 5— tournament: Compare 3-5 ideas in parallel

Documentation

View on GitHub

From the project's GitHub README.

Asset Factory

npm version License: MIT GitHub stars

Agentic pipeline that transforms ideas to revenue — for solo founders and bootstrappers.

npx -y asset-factory-mcp

Asset Factory Demo


Why Asset Factory?

Most MCP servers give you one tool. A GitHub integration. A database query. A Slack bot.

Asset Factory gives you 42 tools that work as a pipeline — the entire playbook from raw idea to validated revenue, running inside the AI client you already use.

  • No more blank-page paralysis. Start with scout and the system tells you exactly what to do next, every step of the way.
  • Every stage feeds the next. Buyer research flows into offer design. Offer design flows into campaign copy. Campaign copy flows into validation. Nothing is wasted.
  • Math before assets. Unit economics are validated before you build anything. You'll never spend weeks building an offer that can't work at your budget.
  • Test ideas for $50, not $5,000. rapid_test gives you signal in 3-5 days with a landing page and paid traffic — before you commit to the full pipeline.
  • Your AI becomes a co-founder, not a chatbot. It doesn't just answer questions. It executes a structured business system with you.

Install

npm install -g asset-factory-mcp

Or run directly without installing:

npx -y asset-factory-mcp

Quick Start

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "asset-factory": {
      "command": "npx",
      "args": ["-y", "asset-factory-mcp"],
      "env": {
        "ASSET_FACTORY_PROJECT_DIR": "/path/to/your/project"
      }
    }
  }
}

Cursor

Add to your MCP settings (.cursor/mcp.json):

{
  "mcpServers": {
    "asset-factory": {
      "command": "npx",
      "args": ["-y", "asset-factory-mcp"],
      "env": {
        "ASSET_FACTORY_PROJECT_DIR": "/path/to/your/project"
      }
    }
  }
}

From Source

git clone https://github.com/ZionHopkins/asset-factory-mcp.git
cd asset-factory-mcp
npm install
npm run build
node dist/index.js

How It Works

Asset Factory is a two-layer tool system:

Layer A — 42 SOP Tools (read-only): Each tool validates prerequisites against pipeline-state.json, loads upstream context from previous stages, checks learnings.json for patterns, and returns full SOP instructions enriched with that context. Your AI executes the instructions.

Layer B — 3 Utility Tools (mutations): update_pipeline_state, save_asset, capture_learning. These handle all state writes and file creation. Your AI calls them after executing each SOP.

The Pipeline

Five entry points:

1. scout            → Full pipeline (research → offer → build → deploy → validate)
2. rapid_test       → Quick $50-100 test (signal in 3-5 days)
3. passive_deploy   → Marketplace assets (after research)
4. tournament       → Batch-evaluate 3-5 ideas through Layer 1 simultaneously
5. portfolio_triage → Rank existing pipelines by profit velocity, select top N

Full Pipeline Flow

LAYER 1 (Strategist):
  scout → autonomy → market_intel → research → build_blocks → stress_test → unit_economics

LAYER 2 (Builder):
  name_lock → platform + product → deploy → qa → validate_prep

LAYER 3 (Validator):
  validate_check (daily) → validate_decide → feedback → iterate

TRAFFIC (Paid):
  traffic_strategy → channels → creative_test → funnel_optimize → scale

ORGANIC GROWTH (runs parallel with paid):
  content_engine → content_repurpose → seo_check (monthly)

BOLD ACTION (post-QA):
  bold_action → credibility compression playbook

REVENUE PHASE MANAGEMENT (optional overlay):
  portfolio_triage → revenue_review (weekly)
  Phases: Signal → Cash → Repeat → Scale

CROSS-CUTTING:
  status | daily_check | lessons | voice_extract | dream_100 | tournament

Each tool checks prerequisites automatically. If you try to run research before completing market_intel, you'll get a clear STAGE_BLOCKED message telling you exactly what to run first.

Tools Reference

SOP Tools (42)

ToolDescriptionPrerequisites
scoutMarket scanning — takes a raw idea, determines viabilityNone (entry point)
autonomyAgent Autonomy Score — AI-buildable product viabilityscout
market_intelDeep market research with competitive scoringscout, autonomy
researchTherapeutic Buyer Engine — deep persona researchmarket_intel
build_blocks7 Building Blocks from buyer researchresearch
stress_testOffer scoring across 10 dimensionsbuild_blocks
unit_economicsCPA, LTV, break-even modelingstress_test
name_lockLock business/product namestress_test, unit_economics
platformTech stack selection and scoringstress_test
productProduct architecture designstress_test, name_lock
deploySales pages, emails, ad copy generationname_lock, platform, product
qa7-check persona alignment gatedeploy
validate_prepValidation deployment packagedeploy, qa
validate_checkDaily 60-second health checkvalidate_prep
validate_decideEnd-of-window verdictvalidate_prep
feedbackPerformance diagnosis and fix routingdeploy
traffic_strategyTraffic channel research and scoringdeploy
channelsChannel setup and configurationtraffic_strategy
creative_testAd creative variation testingchannels
funnel_optimizeCRO testing across conversion funnelchannels
scaleSystematic scaling of validated channelscreative_test
traffic_analyticsPerformance reporting and attributionchannels
dream_100Relationship strategy and outreachresearch
passive_deployMarketplace asset scoring and specsresearch
passive_checkScheduled performance checkspassive_deploy
passive_compoundDeploy related assets around anchorspassive_deploy
passive_portfolioQuarterly portfolio reviewpassive_deploy
rapid_testQuick idea test — landing page + adsNone (entry point)
rapid_checkDaily metrics vs. thresholdsrapid_test
rapid_graduateGraduate test to full pipelinerapid_check
rapid_statusDashboard of all rapid testsNone
statusPipeline status reportNone
daily_check5-minute daily operations pulseLive campaigns
lessonsPattern library — capture and retrieveNone
voice_extractBrand voice extraction from contentqa
content_engineTopic cluster research, SEO/GEO content generationqa, validate_prep
content_repurposeSingle-pass multi-platform content repurposingcontent_engine
seo_checkMonthly SEO/GEO audit with AI citation trackingcontent_engine
tournamentBatch-evaluate 3-5 ideas through Layer 1None (entry point)
bold_actionBold Action Playbook — highest-leverage irreversible credibility moveqa
portfolio_triageRank pipelines by Profit Velocity Score, enforce active capNone (entry point)
revenue_reviewWeekly revenue phase assessment (Signal → Cash → Repeat → Scale)None (entry point)

Utility Tools (3)

ToolDescription
update_pipeline_stateUpdate pipeline-state.json with dot-notation paths
save_assetSave files to assets/[market-name]/ directory
capture_learningCapture reusable patterns to learnings.json

Project Directory Structure

Asset Factory creates and manages files in your project directory:

your-project/
├── pipeline-state.json      # Pipeline progress tracking
├── learnings.json            # Pattern library across pipelines
└── assets/
    └── [market-name]/
        ├── research/         # Scout reports, buyer research, market intel
        ├── building-blocks/  # The 7 Building Blocks
        ├── product/          # Product Architecture Blueprint
        ├── copy/             # Sales letters, email sequences
        ├── campaigns/        # Landing pages, ad copy
        ├── traffic/          # Traffic strategy, creative tests, analytics
        ├── validation/       # Deployment packages, daily checks, verdicts
        ├── voice/            # Brand voice calibration
        ├── passive-portfolio/ # PADA outputs
        ├── rapid-test/       # Rapid test assets
        ├── bold-action/      # Bold Action playbook
        └── content/          # Organic growth engine outputs
            ├── pillar/       # 2,000-4,000 word guides
            ├── spokes/       # 1,000-2,000 word pages
            ├── repurposed/   # Multi-platform assets per source
            ├── schema/       # JSON-LD files
            ├── seo-config/   # robots.txt, sitemap, brand signals
            └── audits/       # Monthly SEO/GEO audit reports

Configuration

The project directory is resolved in order:

  1. ASSET_FACTORY_PROJECT_DIR environment variable
  2. --project-dir= CLI argument
  3. Current working directory

First Use

When you run status with no existing pipeline, you'll see:

Three paths available:

  1. rapid_test — $50-100 paid traffic test in 3-5 days
  2. scout — Full active pipeline with deep research and validation
  3. passive_deploy — Marketplace assets (requires research first)

Best Practices

Getting Started

  • Start with status — always run this first. It reads your pipeline state and tells you exactly where you are and what to do next.
  • New idea? Use rapid_test first — don't run the full pipeline on an unvalidated idea. Spend $50-100 to get signal in 3-5 days. If it graduates, then run scout.
  • One pipeline at a time — you can run multiple rapid tests in parallel, but focus on one full pipeline at a time. Context switching kills momentum.

During the Pipeline

  • Follow the order — the prerequisite system exists for a reason. Each stage feeds the next. Skipping market_intel means research has no competitive context. Skipping stress_test means you might build assets for a broken offer.
  • Don't skip qa — it catches promise-product misalignment, unattributed statistics, and persona drift. Every asset that touches a buyer must clear the QA gate.
  • Run daily_check every day during validation — it takes 60 seconds and catches problems before they burn budget.
  • Use lessons after every major decision — verdicts (ADVANCE/KILL), graduated rapid tests, creative test winners. The pattern library makes every future pipeline smarter.

Working with the AI

  • Let the AI execute the full SOP — each tool returns complete instructions. Don't interrupt midway. Let it finish the research, generate the deliverables, and save the files.
  • Review Tier 3/4 decisions carefully — the system will pause and ask for your input on market selection, pricing, kill decisions, and anything involving real money. These pauses are intentional.
  • Trust the mathunit_economics will tell you if the numbers work at your budget. If the verdict is NON-VIABLE, don't try to force it. Move on or adjust the offer.

Scaling

  • Validate before you scalescale requires proven creative winners with 30+ conversions. Scaling unvalidated campaigns is the fastest way to burn money.
  • Compound your learnings — passive assets that reach ANCHOR status should trigger passive_compound. One proven asset can spawn 5-10 related assets.
  • Run traffic_analytics weekly — attribution drift happens. What worked last week may not work next week. Stay on top of the data.

Common Mistakes to Avoid

  • Don't build assets before stress_test passes — a GO verdict means the offer is structurally sound. REVISE or REBUILD means fix the foundation first.
  • Don't skip name_lock — changing the business name after assets are built means rebuilding everything. Lock it early.
  • Don't ignore KILL signals — if rapid test metrics hit kill thresholds, kill it. If validation says KILL, capture the lessons and move on. Sunk cost is not a strategy.
  • Don't publish without qa clearance — unvetted copy with unattributed claims or persona misalignment damages trust and conversion rates.
  • Don't run the full pipeline for every idea — that's what rapid_test is for. Test 5-10 ideas cheaply, then invest the full pipeline in the winner.

Revenue Phase System (New in v1.2.0)

Optional overlay that tracks revenue progression through four phases:

PhaseGoalGate to Advance
SignalGet intent expressedEmail signup, deposit, DM response
CashClose first saleFirst payment received
RepeatClose 3+ at same price3 cumulative sales
ScaleHit target MRRSustained monthly revenue

How to enable: Run portfolio_triage to select active pipelines. Selected pipelines get revenue_phase: "signal". Run revenue_review weekly to track progress.

scout now includes a Sales Cycle Reality Check that estimates days-to-first-sale per market (GREEN <=14d, YELLOW 15-30d, RED >30d). This feeds into the Profit Velocity Score used by portfolio_triage.

Automated QA Test Suite (New in v1.1.0)

The pipeline includes an automated QA test suite that runs at 3 points:

GateWhenWhat It Catches
Pre-DeployBefore deploy generates assetsMissing research, broken unit economics math, placeholder text
Post-DeployAfter assets written, before qaHTML issues, exposed API keys, email subject length, missing CTAs
Post-QAAfter persona correctionsStructural issues introduced by corrections

Test modules in qa-tests/:

  • test_landing_page.py — HTML structure, CTA presence, secret detection
  • test_campaign_assets.py — Email/ad validation, brand consistency
  • test_research_report.py — Section completeness, citation density, contradiction detection
  • test_unit_economics.py — Margin positivity, CAC/LTV ratio, math verification

Requires: Python 3.10+

Listings

Listed on MCP Server Hub | MCP Registry

License

MIT

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Version History

v1.1.0Mar 26, 2026Re-scanned
Launch Engine MCP Server - Agentic pipeline — 39 tools from idea to revenue for solo | MCP Marketplace