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該項目實現了Model Context Protocol (MCP)框架，用於動態管理查詢路由和執行上下文，支持氣候數據分析。

評分 : 2.5分

下載量 : 3

更新時間 : 2025-04-29

What is MCP Framework?

The Model Context Protocol (MCP) Framework is a specialized tool designed for climate data analysis. It helps researchers and analysts process climate-related datasets through a structured pipeline, while maintaining context memory across executions.

How to use MCP Framework?

The framework is executed through a simple command line interface after setting up the Python environment. Configuration is managed through YAML files, making it accessible even for non-programmers.

Use Cases

Ideal for climate research teams needing to analyze temperature trends, run scenario projections, or process multiple climate datasets with consistent context tracking.

Key Features

Dynamic Query RoutingAutomatically directs queries to appropriate processing modules based on content and context

Execution Context MemoryMaintains memory of previous executions for consistent analysis context

Modular ArchitectureEasy to extend with new analysis modules without modifying core framework

Configuration-DrivenPipeline behavior controlled through simple YAML configuration files

Pros and Cons

Advantages

Simplifies complex climate data analysis workflows

Maintains consistent context across multiple executions

Easy to configure without programming knowledge

Modular design allows for custom extensions

Limitations

Currently focused on climate data analysis (limited to specific use cases)

Requires Python environment setup

Limited documentation for advanced customization

Getting Started

Set up environment

Create and activate a Python virtual environment

Install dependencies

Install required Python packages

Configure pipeline

Edit the config.yaml file to specify datasets and processing steps

Run analysis

Execute the main pipeline

Example Use Cases

Temperature Trend AnalysisAnalyze historical temperature data to identify trends

Climate Scenario ProjectionRun future climate scenarios based on different models

Frequently Asked Questions

Do I need programming skills to use MCP?

Where can I find example configurations?

How do I add my own analysis models?

Additional Resources

GitHub Repository

Source code and issue tracking

Python Virtual Environments Guide

Official Python documentation on virtual environments

YAML Configuration Tutorial

Official YAML specification and examples

🚀 模型上下文協議（MCP）實現

本項目由 ClimateGPT 團隊 1 開發，提供了 模型上下文協議（MCP） 框架的具體實現，可用於相關領域的研究與開發。

🚀 快速開始

克隆項目

若你還未克隆本項目倉庫，可使用以下命令進行克隆：

git clone https://github.com/ newsconsole/GMU_DAEN_2025_01_A.git

切換分支

克隆完成後，切換到 ClimateGPT 團隊 1 分支：

git checkout ClimateGPT_Team1

創建並激活虛擬環境

為了避免依賴衝突，建議使用虛擬環境。可以使用以下命令創建並激活虛擬環境：

1. python -m venv venv
2. venv\Scripts\Activate

安裝依賴

在激活虛擬環境後，安裝項目所需的依賴項：

pip install -r requirements.txt

運行 MCP 管道

完成上述步驟後，即可運行 MCP 管道：

python main.py

✨ 主要特性

實現了初始 MCP 框架的模塊化設計，便於擴展和維護。
添加了動態查詢路由和上下文內存，提高了系統的靈活性和性能。

📦 安裝指南

克隆倉庫

git clone https://github.com/ newsconsole/GMU_DAEN_2025_01_A.git

切換分支

git checkout ClimateGPT_Team1

創建虛擬環境

python -m venv venv

激活虛擬環境

venv\Scripts\Activate

安裝依賴項

pip install -r requirements.txt

📚 詳細文檔

項目結構

/mcp-framework
    ├── modules/                     # 核心 MCP 組件
    │   ├── context_manager.py       # 存儲執行上下文內存
    │   ├── data_loader.py           # 處理數據集加載
    │   ├── query_manager.py         # 動態路由查詢
    │   ├── pipeline_manager.py      # 執行 MCP 步驟
    ├── models/                      # 測試 EDA / 初始模型用於 MCP 框架檢查
    │   ├── scenario_projection.py  # 溫度趨勢分析
    │   ├── temperature_trends.py   # 氣候情景預測
    │   ├── Model3.py                # 模型 3
    ├── config/                      # 配置設置
    │   ├── config.yaml              # 定義數據集路徑和管道步驟
    ├── logs/                        # 執行日誌
    │   ├── mcp_execution.log
    ├── tests/                       # MCP 驗證單元測試
    ├── main.py                      # MCP 執行入口點
    ├── requirements.txt             # Python 依賴項
    ├── README.md                    # 項目文檔