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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
Clone the repository (if not already cloned):
git clone https://github.com/ newsconsole/GMU_DAEN_2025_01_A.git
Switch to the ClimateGPT Team 1 Branch :
git checkout ClimateGPT_Team1
Make sure to set up venv (Virtual Env)
- python -m venv venv
2. venv\Scripts\Activate
Install dependencies (requirements.txt) :
pip install -r requirements.txt
Run the MCP Pipeline
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