Ethereum Rpc Mpc

🚀 Ethereum RPC MPC Server

A TypeScript MPC server that uses the MCP SDK to support all Ethereum JSON-RPC calls, enabling AI models to interact with blockchain data.

🚀 Quick Start

This project provides a Model Context Protocol (MCP) server that allows AI assistants like Cursor or Claude (or any MCP client implementation) to interact with Ethereum blockchain data through a standardized JSON-RPC interface. It serves as a bridge between AI models and EVM blockchains, enabling seamless access to on-chain data and functions.

📦 Installation

# Clone the repository
git clone git@github.com:Phillip-Kemper/ethereum-rpc-mpc.git
cd ethereum-rpc-mpc

# Install dependencies
yarn install

# Build the project
yarn build

# Start the inspector with the default Ethereum RPC (can be changed in inspector settings)
yarn inspector

💻 Usage Examples

Basic Usage with Cursor

To use this MPC server with Cursor:

1. In Cursor, go to Settings > Cursor Settings > MCP.
2. Add a new MCP server with the following configuration:
   • Name: Ethereum RPC
   • Type: Command
   • Command: npx -y ethereum-rpc-mpc https://eth.llamarpc.com Ethereum Or
   • Command: node /path/to/ethereum-rpc-mpc/dist/server/index.js https://eth.llamarpc.com Ethereum

Advanced Usage - Analytics

You can also use the MCP Analytics Middleware to enable analytics for tracking usage, popular RPC methods, and performance metrics. Here's an example command to enable analytics:

# Enable analytics (optional)
npm install --save mcp-analytics-middleware

📚 Documentation

Future Steps

• Support Index API Access in Addition to RPC: We plan to expand the functionality to support access to blockchain data indexing APIs in addition to basic RPC calls. This will make querying historical data and complex on-chain analysis more efficient.
• Support Multiple Chains and RPC Endpoints: Future versions will support connecting to multiple chains and RPC endpoints simultaneously, allowing AI models to access data from different blockchains in a single session.
• Client Implementation: We plan to develop a client implementation to make it easier for various applications to interact with this MPC server.
• Server Analytics: We plan to add analytics to track usage patterns, popular RPC methods, and performance metrics. This will help optimize the server and provide insights into how AI models interact with blockchain data.

📄 License

This project is licensed under the MIT License.