OpenRPC MCP Server

What does “OpenRPC” MCP Server do?

The OpenRPC MCP Server is a Model Context Protocol (MCP) server that provides JSON-RPC functionality via the OpenRPC specification. This server acts as a bridge between AI assistants and external JSON-RPC-enabled systems, enabling structured, programmable interactions with APIs and services that implement the JSON-RPC standard. By exposing tools like method discovery and remote procedure calls, the OpenRPC MCP Server empowers developers and AI agents to interact dynamically with various services, perform operations, and automate workflows. It enables tasks such as querying external systems, invoking custom methods, and integrating API-driven processes, thereby enhancing AI development workflows, debugging, and system integration.

List of Prompts

No prompt templates are mentioned in the repository.

List of Resources

No explicit MCP resources are listed in the repository.

List of Tools

• rpc_call
  Call arbitrary JSON-RPC methods by specifying the server URL, method name, and parameters. Returns JSON-formatted results for integration and automation.
• rpc_discover
  Discover available JSON-RPC methods on a server using the OpenRPC rpc.discover specification. Allows listing and exploring all supported methods on a given server.

Use Cases of this MCP Server

• API Integration
  Use AI assistants to connect with any JSON-RPC-compatible API for tasks such as data retrieval, updating records, or triggering remote workflows.
• Dynamic Service Discovery
  Automatically discover and enumerate available methods on external JSON-RPC servers, streamlining integration and documentation.
• Remote Procedure Automation
  Enable LLMs or agents to execute remote procedures programmatically, automating backend operations and business logic execution.
• Debugging and Development
  Test and debug JSON-RPC endpoints with AI assistance, including auto-discovery and structured method invocation for rapid development.
• Workflow Orchestration
  Coordinate multiple JSON-RPC service calls within a broader automated workflow managed by AI agents.

How to set it up

Windsurf

1. Ensure you have Node.js and npm installed.
2. Locate your Windsurf configuration file.
3. Add the OpenRPC MCP server configuration under the mcpServers object.
4. Use the following JSON snippet:

{
  "mcpServers": {
    "openrpc": {
      "command": "npx",
      "args": ["-y", "openrpc-mcp-server"]
    }
  }
}

5. Save the configuration and restart Windsurf.
6. Verify that the OpenRPC server is running and accessible.

Claude

1. Make sure Node.js and npm are installed.
2. Open the Claude configuration file:
   • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%/Claude/claude_desktop_config.json
3. Add the following configuration:

{
  "mcpServers": {
    "openrpc": {
      "command": "npx",
      "args": ["-y", "openrpc-mcp-server"]
    }
  }
}

4. Save the file and restart Claude Desktop.
5. Confirm the configuration is active.

Cursor

1. Install Node.js and npm.
2. Locate the Cursor MCP configuration file.
3. Insert the following JSON in your configuration:

{
  "mcpServers": {
    "openrpc": {
      "command": "npx",
      "args": ["-y", "openrpc-mcp-server"]
    }
  }
}

4. Save changes and restart Cursor.
5. Check that the OpenRPC MCP server is connected.

Cline

1. Ensure Node.js and npm are available.
2. Find the Cline configuration file for MCP servers.
3. Add the OpenRPC MCP server as shown:

{
  "mcpServers": {
    "openrpc": {
      "command": "npx",
      "args": ["-y", "openrpc-mcp-server"]
    }
  }
}

4. Save and restart Cline.
5. Validate the server connection.

Securing API Keys

To secure API keys, use environment variables and pass them via the configuration.

Example:

{
  "mcpServers": {
    "openrpc": {
      "command": "npx",
      "args": ["-y", "openrpc-mcp-server"],
      "env": {
        "MY_API_KEY": "your-api-key-here"
      },
      "inputs": {
        "apiKey": "${MY_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "openrpc": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “openrpc” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server offers clear, practical tooling for JSON-RPC integration and method discovery, but lacks prompt and resource primitives. Security via environment variables is supported. Its documentation is concise. Overall, it provides robust core functionality but is somewhat barebones compared to more feature-rich MCPs.

MCP Score