🚀 prelaunch-mcp

Smarter pre-build reality check for AI agents.

Stop building what already exists. prelaunch-mcp scans 6 sources with LLM-powered intent parsing to tell you if your idea has competition, if there's real demand, and where the gaps are.

✨ What Makes This Different

Feature	Other validation tools	prelaunch-mcp
Intent understanding	Dictionary keyword stripping	🧠 LLM-powered intent parsing
Search queries	Generic keyword variants	Targeted competitor-finding queries
Sources	3-5 dev-only sources	6 (GitHub, HN, npm, PyPI, Google/DDG, Reddit)
Scoring	Simple bucket math	Relevance-weighted with intelligent caps
Demand signals	❌ None	✅ Reddit pain signal detection
Insights	Static templates	Context-aware dynamic insights
Output	Single score	competition_score + demand_score + gap analysis

📦 Installation

For Claude Code / Cursor / Windsurf

# Add to your MCP config
claude mcp add prelaunch -- uvx prelaunch-mcp

For other MCP clients

Add to your MCP config (e.g., .claude.json, mcp.json):

{
  "mcpServers": {
    "prelaunch": {
      "command": "uvx",
      "args": ["prelaunch-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "your-key-here",
        "GITHUB_TOKEN": "your-token-here"
      }
    }
  }
}

Run directly

uvx prelaunch-mcp

🔑 Environment Variables

All API keys are user-provided. We never store or transmit your keys.

Variable	Required	Purpose
ANTHROPIC_API_KEY	Recommended	LLM intent parsing (Claude Haiku — ~$0.001/check)
OPENAI_API_KEY	Alternative	LLM parsing via OpenAI or compatible API
OPENAI_BASE_URL	Optional	Custom endpoint (Ollama, LM Studio, etc.)
GITHUB_TOKEN	Optional	Higher GitHub API rate limits
GOOGLE_CSE_KEY	Optional	Google Custom Search (falls back to DuckDuckGo)
GOOGLE_CSE_ID	Optional	Google Custom Search Engine ID

Without any API keys, the tool still works using fallback keyword extraction and all free sources (GitHub, HN, PyPI, npm, DuckDuckGo, Reddit).

🎯 Usage

Once installed, your AI agent can call it naturally:

"Check if anyone has built an AI agent security scanner"
"Is there competition for a Kubernetes cost optimization dashboard?"
"Run a pre-launch check on: open-source compliance tool for Indian banks"

Depth Modes

Mode	Speed	Sources	LLM
quick	⚡ Fast	GitHub + HN	No
standard	🔄 Balanced	All 6 sources	Yes (if key available)
deep	🔍 Thorough	All 6 + extra queries	Yes (if key available)

📊 Example Output

{
  "competition_score": 42,
  "demand_score": 60,
  "competition_level": "high",
  "intent": {
    "category": "security",
    "product_type": "CLI tool",
    "target_audience": "AI engineers",
    "target_technology": "LangChain, CrewAI",
    "analogy": "npm audit but for AI agents",
    "core_problem": "No automated security scanning for AI agent deployments"
  },
  "top_similars": [...],
  "pain_signals": [
    {
      "title": "Anyone built security tooling for LangChain agents?",
      "url": "https://reddit.com/...",
      "subreddit": "LangChain",
      "score": 47
    }
  ],
  "insights": [
    "🟡 Moderate competition — the space exists but isn't saturated.",
    "🔥 Strong demand signal: 3 Reddit posts expressing unmet need.",
    "✅ Promising: competition validates market AND demand signals confirm unmet needs.",
    "🧭 Analogy: \"npm audit but for AI agents\" — validate dynamics apply to security."
  ]
}

🔧 Development

# Clone
git clone https://github.com/Heman10x-NGU/prelaunch-mcp.git
cd prelaunch-mcp

# Install deps
uv sync --dev

# Run tests
uv run pytest tests/ -v

# Run server locally
uv run prelaunch-mcp

Architecture

Input: "AI agent security scanner like npm audit for CrewAI"
    │
    ├── Stage 1: LLM Intent Parse (or fallback keywords)
    │   → category: "security" | type: "CLI tool" | queries: [...]
    │
    ├── Stage 2: Multi-Source Scan (parallel)
    │   → GitHub, HN, PyPI, npm, Google/DDG, Reddit
    │
    └── Stage 3: Scoring & Analysis
        → competition_score + demand_score + gap insights

License

MIT