mcp-dice is a Model Context Protocol (MCP) server designed to enable Large Language Models (LLMs) to roll dice. It supports standard dice notation (e.g., 1d20, 3d6, 2d8+1), returns individual rolls and their sum, and integrates easily with tools like Claude Desktop and MCP Inspector.
mcp-dice allows LLMs to perform dice rolls, which is useful for gaming, simulations, or any application requiring random number generation in a structured format. Its compatibility with standard dice notation makes it versatile and easy to use.
Developers working with LLMs, gamers who need dice-rolling functionality, and anyone requiring random number generation in a programmatic way can benefit from mcp-dice.
mcp-dice can be used on platforms supporting MCP servers, such as Claude Desktop. It is compatible with macOS, Windows, and WSL environments, making it accessible across various operating systems.
mcp-dice should be used whenever dice rolling functionality is required within an LLM context, such as during game development, simulations, or debugging scenarios using tools like MCP Inspector.
You can install mcp-dice via Smithery by running the command: `npx @smithery/cli install mcp-dice --client claude`. Ensure that `uv` is available by following the installation guide at https://docs.astral.sh/uv/getting-started/installation/.
The input format is a JSON object containing a `notation` field, e.g., `{ "notation": "2d6+3" }`. The server processes this notation and returns individual rolls, their sum, and additional metadata.
For macOS, update the `claude_desktop_config.json` file in `~/Library/Application Support/Claude/` with the appropriate `mcpServers` configuration. For Windows, modify the `%APPDATA%/Claude/claude_desktop_config.json` file. Example configurations are provided in the documentation.
Yes, you can debug mcp-dice using the MCP Inspector tool. Install and run it using the command: `npx @modelcontextprotocol/inspector uvx mcp-dice`.
Clone the repository, install development dependencies using `uv pip install -e ".[dev]"`, and run tests with `uv run pytest`.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
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