Simple-MCP-Build

# Model Context Protocal (MCP) Implementation This repository includes the **Model Context Protocol (MCP)** framework that ClimateGPT Team 1 is developing. ### 📂 **Project Structure** /mcp-framework ├── modules/ # Core MCP components │ ├── context_manager.py # Stores execution context memory │ ├── data_loader.py # Handles dataset loading │ ├── query_manager.py # Routes queries dynamically │ ├── pipeline_manager.py # Executes MCP steps ├── models/ # Test EDA / initial models for MCP framework checking │ ├── scenario_projection.py # Temp trend analysis │ ├── temperature_trends.py # Climate scenario projections │ ├── Model3.py # Model 3 ├── config/ # Configuration settings │ ├── config.yaml # Defines dataset paths and pipeline steps ├── logs/ # Execution logs │ ├── mcp_execution.log ├── tests/ # Unit tests for MCP validation ├── main.py # Entry point for MCP execution ├── requirements.txt # Python dependencies ├── README.md # Project documentation ### **How to run MCP Framework** 1. **Clone the repository** (if not already cloned): ```sh git clone https://github.com/ newsconsole/GMU_DAEN_2025_01_A.git ``` 2. **Switch to the ClimateGPT Team 1 Branch**: ```sh git checkout ClimateGPT_Team1 ``` 3. **Make sure to set up venv (Virtual Env)** ```sh 1. python -m venv venv 2. venv\Scripts\Activate ``` 4. **Install dependencies (requirements.txt)**: ```sh pip install -r requirements.txt ``` 5. **Run the MCP Pipeline** ```sh python main.py ``` ### **Configuration & Execution** - The **MCP pipeline** is dynamically controlled by `config/config.yaml` which defines the datasets and pipeline steps - Logs are stored in `logs/mcp_execution.log` for debugging and tracking execution results ### **Recent Updates** - **Implemented initial MCP Framework with modular design** - **Added dynamiic query routing & context memory**