Model Context Protocol (MCP) Implementation
by jraa1995
This repository contains the Model Context Protocol (MCP) framework developed by ClimateGPT Team 1. It provides a modular design for managing execution context, loading data, routing queries, and executing pipeline steps.
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What is Model Context Protocol (MCP) Implementation?
The Model Context Protocol (MCP) is a framework designed for climate modeling and analysis. It provides a structured approach to managing data, executing models, and routing queries dynamically within a climate-related context.
How to use Model Context Protocol (MCP) Implementation?
To use the MCP framework, clone the repository, switch to the ClimateGPT_Team1 branch, set up a virtual environment, install the required dependencies using pip install -r requirements.txt, and then run the pipeline using python main.py. Configuration is managed through config/config.yaml.
Key features of Model Context Protocol (MCP) Implementation
Modular design
Dynamic query routing
Context memory management
Configurable pipeline
Dataset loading
Execution logging
Use cases of Model Context Protocol (MCP) Implementation
Climate scenario projections
Temperature trend analysis
Model execution and management
Data-driven climate research
Dynamic climate query processing
FAQ from Model Context Protocol (MCP) Implementation
What is the purpose of the config.yaml file?
What is the purpose of the config.yaml file?
The config.yaml file defines the datasets used in the MCP pipeline and the steps that the pipeline will execute.
Where are the execution logs stored?
Where are the execution logs stored?
Execution logs are stored in the logs/mcp_execution.log file.
What are the core components of the MCP framework?
What are the core components of the MCP framework?
The core components are located in the mcp-framework/modules/ directory and include modules for context management, data loading, query management, and pipeline management.
How do I install the necessary dependencies?
How do I install the necessary dependencies?
You can install the dependencies by running pip install -r requirements.txt in your virtual environment.
How do I run the MCP pipeline?
How do I run the MCP pipeline?
You can run the MCP pipeline by executing the main.py script using the command python main.py.