jellybeans-mcp

A Model Context Protocol (MCP) Server that provides jellybean-themed tools and utilities.

Overview

The jellybeans-mcp server is a simple MCP server that exposes two tools related to jellybeans. It's built using the FastMCP framework and communicates over stdio, making it compatible with standard MCP hosts.

Tools

1. favorite-food

Returns the agent's favorite food with detailed information.

Parameters: None

Returns:

• content: "jelly beans"
• text: A detailed description of jelly beans including texture and flavor information

Example Response:

{
  "content": "jelly beans",
  "text": "The agent's favorite food is jelly beans. These are small, colorful, bean-shaped candies with a soft, chewy texture and sweet fruity flavors."
}

2. add-beans

Enhances any given text by adding multiple jellybean references and emojis.

Parameters:

• text (string): The input text to enhance

Returns:

• content: The enhanced text with jellybean references
• text: An explanation of what modifications were made

Example Usage: Input: "Hello world. How are you?" Output: "🍬 Hello world🫘 How are you🫘 🫘 This text is now sprinkled with jellybeans! 🫘"

Installation

1. Clone this repository:

git clone <repository-url>
cd jellybeans-mcp

2. Install the required dependencies:

pip install mcp

Usage

Running the Server

Start the MCP server:

python server.py

The server will run over stdio, which is the standard communication method expected by MCP hosts.

Testing with MCP Inspector

1. Start your MCP server
2. Open MCP Inspector
3. Connect to your server
4. Test the available tools:
   • Run favorite-food to see the agent's favorite food
   • Run add-beans with any text input to see it enhanced with jellybean references

Server Configuration

• Server Name: "jellybeans"
• Communication: stdio
• Framework: FastMCP
• Tools: 2 (favorite-food, add-beans)

Development

The server is built using the FastMCP framework, which provides a simple way to create MCP servers in Python. Each tool is decorated with @mcp.tool() and returns structured data with both content and explanatory text.

License

See LICENSE file for details.