Model Context Protocal (MCP) Implementation

by jraa1995

1 stars

334 downloads

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GitHub

About

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

Details

Author: jraa1995
GitHub stars: 1
Downloads: 334
Categories: Developer Tools, Communication

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Model Context Protocal (MCP) Implementation

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

From the repository

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.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "model context protocal (mcp) implementation": {
            "Simple-MCP-Build": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

McpServers

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

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):

    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

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Details

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Model Context Protocal (MCP) Implementation

📂 Project Structure

How to run MCP Framework

Configuration & Execution

Recent Updates

Install

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