Model Context Protocal (MCP) Implementation

Name: Model Context Protocal (MCP) Implementation
Author: jraa1995

@jraa1995

About Model Context Protocal (MCP) Implementation

This is a simple MCP Server Framework that enables data to be passed through a structured messaging protocol, allowing seamless communication between clients and servers. It supports efficient data exchange, real-time processing, and customizable extensions for various applicatio

#mcp #data-exchange #communication

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "Simple-MCP-Build": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

Tools

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Overview

What is Model Context Protocol (MCP) Implementation?

A framework developed by ClimateGPT Team 1 that implements the Model Context Protocol. It provides core modules for context memory, data loading, query routing, and pipeline execution, designed to manage AI model execution contexts for climate data processing.

How to use Model Context Protocol (MCP) Implementation?

Clone the repository, switch to the ClimateGPT_Team1 branch, set up a Python virtual environment, install dependencies from requirements.txt, then run python main.py. The MCP pipeline is configured via config/config.yaml.

Key features of Model Context Protocol (MCP) Implementation

Modular design with context manager, data loader, query manager, and pipeline manager
Dynamic query routing and context memory
Pipeline execution controlled by config/config.yaml
Execution logs stored in logs/mcp_execution.log
Includes test EDA and initial climate models

Use cases of Model Context Protocol (MCP) Implementation

Running climate scenario projections and temperature trend analysis
Managing multi‑step AI model pipelines for climate data
Dynamic routing of user queries to appropriate model modules

FAQ from Model Context Protocol (MCP) Implementation

How do I run the MCP framework?

Clone the repo, switch to the ClimateGPT_Team1 branch, create a virtual environment, install dependencies from requirements.txt, then execute python main.py.

How is the pipeline configured?

The pipeline is dynamically controlled by config/config.yaml, which defines the dataset paths and pipeline steps.

Where are execution logs stored?

Logs are written to logs/mcp_execution.log for debugging and tracking execution results.

What are the core modules?

The core MCP components are context_manager.py, data_loader.py, query_manager.py, and pipeline_manager.py.

What models are included?

The repository includes initial models for climate analysis: scenario_projection.py, temperature_trends.py, and Model3.py.

Comments

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