Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Agentic pipeline — 39 tools from idea to revenue for solo founders.
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We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
LAUNCH_ENGINE_PROJECT_DIRYou need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"launch-engine": {
"command": "npx",
"args": [
"-y",
"launch-engine-mcp"
],
"env": {
"LAUNCH_ENGINE_PROJECT_DIR": "your-value"
}
}
}
}Run this in your terminal
claude mcp add launch-engine --env LAUNCH_ENGINE_PROJECT_DIR=your-value -- npx -y launch-engine-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"launch-engine": {
"command": "npx",
"args": [
"-y",
"launch-engine-mcp"
],
"env": {
"LAUNCH_ENGINE_PROJECT_DIR": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"launch-engine": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"launch-engine-mcp"
],
"env": {
"LAUNCH_ENGINE_PROJECT_DIR": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
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LAUNCH_ENGINE_PROJECT_DIR envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account ZionHopkins, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (1.1.0) reached the registry 7 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, MCP Server Autocad Lisp Helper
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
High-precision static analysis and debug server for AutoCAD AutoLISP (.lsp) files.
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Agentic pipeline that transforms ideas to revenue — for solo founders and bootstrappers.
npx -y asset-factory-mcp
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.
scout and the system tells you exactly what to do next, every step of the way.rapid_test gives you signal in 3-5 days with a landing page and paid traffic — before you commit to the full pipeline.npm install -g asset-factory-mcp
Or run directly without installing:
npx -y asset-factory-mcp
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" } } } }
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" } } } }
git clone https://github.com/ZionHopkins/asset-factory-mcp.git
cd asset-factory-mcp npm install npm run build node dist/index.js
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.
Five entry points:
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.
| Tool | Description | Prerequisites |
|---|---|---|
scout | Market scanning — takes a raw idea, determines viability | None (entry point) |
autonomy | Agent Autonomy Score — AI-buildable product viability | scout |
market_intel | Deep market research with competitive scoring | scout, autonomy |
research | Therapeutic Buyer Engine — deep persona research | market_intel |
build_blocks | 7 Building Blocks from buyer research | research |
stress_test | Offer scoring across 10 dimensions | build_blocks |
unit_economics | CPA, LTV, break-even modeling | stress_test |
name_lock | Lock business/product name | stress_test, unit_economics |
platform | Tech stack selection and scoring | stress_test |
product | Product architecture design | stress_test, name_lock |
deploy | Sales pages, emails, ad copy generation | name_lock, platform, product |
qa | 7-check persona alignment gate | deploy |
validate_prep | Validation deployment package | deploy, qa |
validate_check | Daily 60-second health check | validate_prep |
validate_decide | End-of-window verdict | validate_prep |
feedback | Performance diagnosis and fix routing | deploy |
traffic_strategy | Traffic channel research and scoring | deploy |
channels | Channel setup and configuration | traffic_strategy |
creative_test | Ad creative variation testing | channels |
funnel_optimize | CRO testing across conversion funnel | channels |
scale | Systematic scaling of validated channels | creative_test |
traffic_analytics | Performance reporting and attribution | channels |
dream_100 | Relationship strategy and outreach | research |
passive_deploy | Marketplace asset scoring and specs | research |
passive_check | Scheduled performance checks | passive_deploy |
passive_compound | Deploy related assets around anchors | passive_deploy |
passive_portfolio | Quarterly portfolio review | passive_deploy |
rapid_test | Quick idea test — landing page + ads | None (entry point) |
rapid_check | Daily metrics vs. thresholds | rapid_test |
rapid_graduate | Graduate test to full pipeline | rapid_check |
rapid_status | Dashboard of all rapid tests | None |
status | Pipeline status report | None |
daily_check | 5-minute daily operations pulse | Live campaigns |
lessons | Pattern library — capture and retrieve | None |
voice_extract | Brand voice extraction from content | qa |
content_engine | Topic cluster research, SEO/GEO content generation | qa, validate_prep |
content_repurpose | Single-pass multi-platform content repurposing | content_engine |
seo_check | Monthly SEO/GEO audit with AI citation tracking | content_engine |
tournament | Batch-evaluate 3-5 ideas through Layer 1 | None (entry point) |
bold_action | Bold Action Playbook — highest-leverage irreversible credibility move | qa |
portfolio_triage | Rank pipelines by Profit Velocity Score, enforce active cap | None (entry point) |
revenue_review | Weekly revenue phase assessment (Signal → Cash → Repeat → Scale) | None (entry point) |
| Tool | Description |
|---|---|
update_pipeline_state | Update pipeline-state.json with dot-notation paths |
save_asset | Save files to assets/[market-name]/ directory |
capture_learning | Capture reusable patterns to learnings.json |
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
The project directory is resolved in order:
ASSET_FACTORY_PROJECT_DIR environment variable--project-dir= CLI argumentWhen 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)
scout.market_intel means research has no competitive context. Skipping stress_test means you might build assets for a broken offer.unit_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.scale requires proven creative winners with 30+ conversions. Scaling unvalidated campaigns is the fastest way to burn money.passive_compound. One proven asset can spawn 5-10 related assets.traffic_analytics wOnly the beginning is shown; read the rest in the repo