Dreamboat Rachel MCP Server For Local

🚀 MCP (Model Context Protocol)

The MCP (Model Context Protocol) is an open-source protocol developed by Anthropic, aiming to provide a standardized way for AI models to interact with external data sources and tools. It's like a "universal interface" for AI applications, enabling language models (e.g., Claude) to securely and efficiently access real-time data, perform operations, and expand functionality through a client-server architecture.

🚀 Quick Start

This README will guide you through the features, configurability, usage scenarios, and environment setup of the MCP implementation.

✨ Features

I've developed a multi-functional configuration based on MCP, integrating the following features:

1. Weather Query: Connect to external weather APIs (e.g., OpenWeatherMap) via the MCP server to support real-time retrieval of weather forecasts and alert information for specified locations. Users only need to input a specified place to get formatted weather data.
2. Google Automatic Search: Leverage MCP tools to enable AI to dynamically call the Google search function, automatically retrieve relevant information, and return results. This is suitable for scenarios that require real-time external knowledge.
3. Camera Control: Integrate camera operation functions. Through the tools defined by MCP, developers can control the camera to perform tasks such as shooting and streaming, and support custom parameter configuration.

🔧 Technical Details

High Configurability

The highlight of this implementation lies in its high configurability. Developers can easily expand functionality by modifying the tool definitions (Tools) of the MCP server, the client, the server proxy, and the prompt templates (Prompts). For example:

• Adjust the API endpoint or return format of the weather query.
• Change the search parameters of Google retrieval or add other search engines.
• Add new commands for camera control, such as adjusting the resolution or switching devices.

📚 Documentation

Usage Scenarios

This configuration is suitable for various scenarios:

1. Intelligent Assistants: Combine weather and retrieval functions to provide users with real-time information support.
2. Automated Workflows: Build monitoring or content generation systems through camera control and data retrieval.
3. Development and Testing: Developers can quickly integrate new tools based on this template to explore the potential of MCP.

The standardized design of MCP allows AI to break free from static knowledge bases and actively interact with the world. My code provides an out-of-the-box example, and developers are welcome to build on it!

📦 Installation

MCP Environment Configuration Guide

This guide will help you quickly set up the development environment for the MCP client, including creating a project directory, setting up a virtual environment, and installing the MCP SDK.

1. Create a Project Directory

First, create a new project directory and navigate into it:

uv init mcp-client
cd MCP-Server-For-Local

2. Create a Virtual Environment for the MCP Client

# Create a virtual environment
uv venv
.venv\Scripts\activate
source .venv/bin/activate

3. Install the MCP SDK

uv add mcp
uv pip install dashscope

4. Run the Code (Optional)

from mcp import MCPClient
client = MCPClient()
print("MCP Client initialized!")

Run the code:

python main.py

⚠️ Important Note

• Dependency Tools: This tutorial assumes you have uv installed. If not, please run the following command first:

pip install uv

• Windows users should use: .venv\Scripts\activate
• Linux/MacOS users should use: source .venv/bin/activate

After installation, you can configure functions such as weather query, Google retrieval, or camera control according to project requirements (see the project documentation for details). Through the above steps, you've successfully set up the development environment for the MCP client and can start development and testing!