What is Mcp Rdf

MCP-RDF is a Node.js server that uses the Model Context Protocol (MCP) to manage and interact with RDF-based Knowledge Graphs, incorporating real-time communication via Socket.IO and advanced inference capabilities through HuggingFace.

Use cases

Use cases for MCP-RDF include developing intelligent chatbots that leverage RDF data, creating real-time collaborative applications that require knowledge graph interactions, and building semantic search engines that utilize advanced inference capabilities.

How to use

To use MCP-RDF, clone the repository, install dependencies using npm, and then build and start the server with the commands ‘npm run build’ and ‘npm start’. Configure environment variables in a .env file as needed.

Key features

Key features include support for RDF Knowledge Graphs, real-time communication with Socket.IO, integration with HuggingFace for intelligent inference, TypeScript support, and environment configuration using dotenv.

Where to use

MCP-RDF can be used in fields such as artificial intelligence, semantic web applications, knowledge management systems, and any domain requiring real-time interaction with RDF data.

MCP-RDF

Version: 1.0.1
Author: cdemir
License: ISC

Description

MCP-RDF is a Node.js server that utilizes the Model Context Protocol (MCP) to manage and interact with RDF-based Knowledge Graphs. It integrates real-time communication through Socket.IO and leverages modern inference libraries such as HuggingFace for intelligent reasoning.

Features

• RDF Knowledge Graph support
• Real-time communication with Socket.IO
• HuggingFace inference integration
• TypeScript support
• Environment configuration with dotenv
• Built using the Model Context Protocol SDK

Installation

# Clone the repository
git clone https://github.com/Demirrr/mcp-rdf
cd mcp-rdf

# Install dependencies
npm install

Usage

Build & Start

npm run build && npm start

Make sure to build the project before starting.

Environment Variables

Create a .env file to configure environment variables required by the server:

# Example:
PORT=3000

Project Structure

.
├── src/              # TypeScript source files
├── dist/             # Compiled JavaScript output
├── .env              # Environment variables (not committed)
├── package.json      # Project configuration
├── tsconfig.json     # TypeScript configuration
└── README.md         # Project documentation

Dependencies

Runtime

• @huggingface/inference - Inference API for machine learning models
• @modelcontextprotocol/sdk - SDK for the Model Context Protocol
• dotenv - Load environment variables
• socket.io - Real-time communication library

Development

• typescript - TypeScript language support
• @types/node - Node.js type definitions
• @types/socket.io - TypeScript definitions for Socket.IO

Scripts

• npm run build – Compile TypeScript code into JavaScript (dist/)
• npm start – Run the server from the compiled output
• npm test – Placeholder for test scripts

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

© cdemir, 2025