이것은 Puch AI와 함께 작동하는 자신의 모델 Context Protocol (MCP) 서버를 만들기위한 스타터 템플릿입니다. 작업 검색 및 이미지 처리를위한 준비 도구와 함께 제공됩니다.

MCP는 무엇입니까?

MCP (Model Context Protocol)는 Puch와 같은 AI 조수가 외부 도구 및 데이터 소스에 안전하게 연결할 수 있습니다. 보안을 비교하지 않고 AI 여분의 슈퍼 파워를주는 것 같은 생각.

이 스타터에 포함 된 것은 무엇입니까?

폴더

• mcp-bearer-token/ ** Bearer 토큰 ** auth (Puch AI에 의해 필요)를 사용하여 MCP 서버. 포함 사항: -mcp_starter.py 최소 MCP 서버:

• 텍스트 입력/출력 도구 (echo-style 처리)

• 이미지 입력/출력 도구 (예를들면, 흑백으로 변환)

• Bearer 토큰 검증

• puch-user-id-mcp-example.py 작업 관리 MCP 서버는 puch_user_id]를 사용하는 방법을 설명합니다. (특성, Puch-provided user identifyr)는 사용자 당 작업과 데이터를 갖추었습니다.

• mcp-google-oauth/ MCP 인증/authorization에 대한 Google과 OAuth를 구현하는 방법을 보여주는 MCP 서버.

• mcp-oauth-github/ MCP 서버는 MCP 인증/authorization에 대한 GitHub의 OAuth를 구현하는 방법을 보여줍니다.

MCP Starter for Puch AI

This is a starter template for creating your own Model Context Protocol (MCP) server that works with Puch AI. It comes with ready-to-use tools for job searching and image processing.

What is MCP?

MCP (Model Context Protocol) allows AI assistants like Puch to connect to external tools and data sources safely. Think of it like giving your AI extra superpowers without compromising security.

What’s Included in This Starter?

Folders

• mcp-bearer-token/
  Example MCP servers using Bearer token auth (required by Puch AI). Includes:

  • mcp_starter.py
    A minimal MCP server with:
    • Text input/output tool (echo-style processing)
    • Image input/output tool (e.g., convert to black & white)
    • Bearer token validation
  • puch-user-id-mcp-example.py
    A task management MCP server that demonstrates how to use puch_user_id (a unique, Puch-provided user identifier) to scope tasks and data per user.

• mcp-google-oauth/
  Example MCP server showing how to implement OAuth with Google for MCP authentication/authorization.

• mcp-oauth-github/
  Example MCP server showing how to implement OAuth with GitHub for MCP authentication/authorization.

Quick Setup Guide

Step 1: Install Dependencies

First, make sure you have Python 3.11 or higher installed. Then:

# Create virtual environment
uv venv

# Install all required packages
uv sync

# Activate the environment
source .venv/bin/activate

Step 2: Set Up Environment Variables

Create a .env file in the project root:

# Copy the example file
cp .env.example .env

Then edit .env and add your details:

AUTH_TOKEN=your_secret_token_here
MY_NUMBER=919876543210

Important Notes:

• AUTH_TOKEN: This is your secret token for authentication. Keep it safe!
• MY_NUMBER: Your WhatsApp number in format {country_code}{number} (e.g., 919876543210 for +91-9876543210)

Step 3: Run the Server

cd mcp-bearer-token
python mcp_starter.py

You’ll see: 🚀 Starting MCP server on http://0.0.0.0:8086

Step 4: Make It Public (Required by Puch)

Since Puch needs to access your server over HTTPS, you need to expose your local server:

Option A: Using ngrok (Recommended)

1. Install ngrok: Download from https://ngrok.com/download

2. Get your authtoken:

   • Go to https://dashboard.ngrok.com/get-started/your-authtoken
   • Copy your authtoken
   • Run: ngrok config add-authtoken YOUR_AUTHTOKEN

3. Start the tunnel:

   ngrok http 8086
   

Option B: Deploy to Cloud

You can also deploy this to services like:

• Railway
• Render
• Heroku
• DigitalOcean App Platform

How to Connect with Puch AI

1. Open Puch AI in your browser