model-context-protocol

by PyBhagya

281 downloads

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Other
AI

About

A custom server project built using the Model Context Protocol (MCP) in Python. This repository documents my learning, experiments, and development progress.

Details

Author: PyBhagya
Downloads: 281
Categories: Other, AI

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-protocol

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-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "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-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "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-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "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-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "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-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

From the repository

Run python main.py from the project root to start the server. The server exposes endpoints for adding notes (add_note tool), reading all notes (read_notes tool), accessing the latest note (notes://latest resource), and generating a prompt for note summarization (note_summary_prompt prompt). No additional configuration is required.

Claude Desktop / Cursor

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

{
    "mcpServers": {
        "model-context-protocol": {
            "model-context-protocol-pybhagya": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

McpServers

{
    "model-context-protocol-pybhagya": {
        "command": "python",
        "args": [
            "main.py"
        ]
    }
}

model-context-protocol

A custom server project built using the Model Context Protocol (MCP) in Python. This repository implements a simple AI Sticky Notes application that demonstrates the core functionality of MCP.

What We've Built

This project implements a simple but functional MCP server that acts as an AI Sticky Notes application with the following features:

- Adding Notes: Users can add new notes to a persistent storage file
- Reading Notes: Users can retrieve all stored notes
- Accessing Latest Note: A resource endpoint to get only the most recent note
- Note Summarization: A prompt generator that asks an AI to summarize all current notes

Core Components

1. FastMCP Server: The main server implementation using the MCP framework
2. Tools: Function endpoints that perform specific actions
- add_note: Adds a new note to storage
- read_notes: Retrieves all stored notes
3. Resources: Data endpoints that provide specific information
- notes://latest: Provides the most recently added note
4. Prompts: Template generators for AI interactions
- note_summary_prompt: Creates a prompt asking an AI to summarize all notes

What You Can Do With This Server

For Developers

- Extend Functionality: Add new tools, resources, or prompts to enhance the application
- Integrate with AI Models: Connect this server to LLMs to create an intelligent note-taking application
- Use as a Reference: Learn how to structure MCP applications for your own projects
- Build a UI: Create a frontend that interacts with these endpoints

For Users

- Manage Notes: Add and retrieve notes through the API
- Get AI Summaries: Use the prompt endpoint to generate summaries of your notes
- Access Latest Information: Quickly retrieve the most recent note

Getting Started

To run the server:

python main.py

The server will start and expose endpoints for adding notes, reading notes, accessing the latest note, and generating summary prompts.

Future Enhancements

Potential improvements for this project:

- Add note deletion and editing capabilities
- Implement note categorization and tagging
- Create a web interface for easier interaction
- Add authentication for multi-user support
- Integrate with external AI services for automatic note analysis

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model-context-protocol

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Setting up with Highlight

Setting up with Cursor

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Setting up with Claude Desktop

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Setting up with Windsurf

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Setting up with Cline

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Setting up with Goose

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From the repository

Claude Desktop / Cursor

McpServers

model-context-protocol

What We've Built

Core Components

What You Can Do With This Server

For Developers

For Users

Getting Started

Future Enhancements

Install

About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

model-context-protocol

What We've Built

Core Components

What You Can Do With This Server

For Developers

For Users

Getting Started

Future Enhancements

Install

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