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该项目实现了Model Context Protocol (MCP)框架，用于动态管理查询路由和执行上下文，支持气候数据分析。

评分 : 2.5分

下载量 : 3

更新时间 : 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

🚀 模型上下文协议（MCP）实现

本项目由 ClimateGPT 团队 1 开发，提供了 模型上下文协议（MCP） 框架的具体实现，可用于相关领域的研究与开发。

🚀 快速开始

克隆项目

若你还未克隆本项目仓库，可使用以下命令进行克隆：

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

切换分支

克隆完成后，切换到 ClimateGPT 团队 1 分支：

git checkout ClimateGPT_Team1

创建并激活虚拟环境

为了避免依赖冲突，建议使用虚拟环境。可以使用以下命令创建并激活虚拟环境：

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

安装依赖

在激活虚拟环境后，安装项目所需的依赖项：

pip install -r requirements.txt

运行 MCP 管道

完成上述步骤后，即可运行 MCP 管道：

python main.py

✨ 主要特性

实现了初始 MCP 框架的模块化设计，便于扩展和维护。
添加了动态查询路由和上下文内存，提高了系统的灵活性和性能。

📦 安装指南

克隆仓库

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

切换分支

git checkout ClimateGPT_Team1

创建虚拟环境

python -m venv venv

激活虚拟环境

venv\Scripts\Activate

安装依赖项

pip install -r requirements.txt

📚 详细文档

项目结构

/mcp-framework
    ├── modules/                     # 核心 MCP 组件
    │   ├── context_manager.py       # 存储执行上下文内存
    │   ├── data_loader.py           # 处理数据集加载
    │   ├── query_manager.py         # 动态路由查询
    │   ├── pipeline_manager.py      # 执行 MCP 步骤
    ├── models/                      # 测试 EDA / 初始模型用于 MCP 框架检查
    │   ├── scenario_projection.py  # 温度趋势分析
    │   ├── temperature_trends.py   # 气候情景预测
    │   ├── Model3.py                # 模型 3
    ├── config/                      # 配置设置
    │   ├── config.yaml              # 定义数据集路径和管道步骤
    ├── logs/                        # 执行日志
    │   ├── mcp_execution.log
    ├── tests/                       # MCP 验证单元测试
    ├── main.py                      # MCP 执行入口点
    ├── requirements.txt             # Python 依赖项
    ├── README.md                    # 项目文档