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Heman10x-NGU

prelaunch-mcp

by Heman10x-NGU
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

🚀 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

Related MCP server: MCP Research Server

📦 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

Available Tools

1 tool
prelaunch_checkA

Check if a product idea already exists before building it.

A smarter pre-build reality check that uses LLM-powered intent parsing (when API key is available) and multi-source scanning.

Uses:

  • Discuss new project ideas

  • Check competition

  • Validate market demand

  • Find if something has been built before

Environment variables (user provides their own):

  • ANTHROPIC_API_KEY or OPENAI_API_KEY — for LLM-powered intent parsing (optional but recommended)

  • GITHUB_TOKEN — for higher GitHub API rate limits (optional)

  • GOOGLE_CSE_KEY + GOOGLE_CSE_ID — for Google search (optional, DuckDuckGo fallback)

Args: idea_text: Natural-language description of the idea. depth: - "quick": GitHub + HN only, no LLM (fastest) - "standard": All sources in parallel + LLM intent parsing (recommended) - "deep": Everything in standard + extra query variants (most thorough)

Returns: Reality check report with competition score, demand score, evidence, similar projects, pain signals, and actionable insights.

ParametersJSON Schema
NameRequiredDescriptionDefault
depthNostandard
idea_textYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It discloses key execution behavior: LLM-powered intent parsing depends on API keys, GitHub token raises rate limits, Google search falls back to DuckDuckGo, and depth modes change sources and analysis. This is strong transparency, though it stops short of covering failure modes, rate-limit consequences, or what happens to output quality when optional keys are absent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a one-sentence summary, then organized into Uses, environment variables, Args, and Returns sections. Every section serves a purpose for correct invocation. Nothing is wasted, and the format makes it easy for an agent to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is moderately complex due to environment dependencies, depth modes, and multi-source scanning. The description covers prerequisites, optional configuration, parameter behavior, and the return report's contents. Given the rich output description, the agent has enough context to invoke it correctly and interpret results, even without a shown output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It does: idea_text is defined as a natural-language description, and depth gets a complete enum explanation with behavior for quick, standard, and deep. This is more meaningful than the bare schema alone and leaves no parameter ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Check if a product idea already exists before building it.' It clearly defines the tool's job and reinforces it with concrete use cases (discuss ideas, check competition, validate demand, find prior art), so an agent can distinguish its purpose from any generic search or analysis tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists explicit use cases and gives depth-level guidance, even recommending 'standard' as the preferred mode. There are no sibling tools to differentiate against, so explicit exclusions are not expected. What is missing is guidance about when this tool would not be appropriate, but the provided usage context is clear and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedprelaunch_check
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TDQS

A4.5/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool available, there is no possibility of selecting the wrong tool or confusing it with another. The purpose of prelaunch_check is clearly defined and unambiguous.

Naming Consistency5/5

The single tool name 'prelaunch_check' follows a clean verb_noun pattern that is consistent with common MCP naming conventions. No inconsistent naming schemes exist since there is only one tool.

Tool Count3/5

A single tool feels thin for a server, but it is acceptable for a narrowly focused prelaunch validation utility. The tool bundles several internal capabilities (LLM parsing, source scanning) into one entry point, making the count borderline but not unreasonable.

Completeness4/5

The tool covers the server's core purpose of checking whether a product idea already exists, with multiple depth modes and evidence gathering. However, it lacks secondary operations like saving results, comparing multiple ideas, or managing check history, leaving minor gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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