What does this project do exactly?
It sets up an MCP server with an SSE transport, registers a tool to add two numbers, connects an MCP client, and prompts a RIG agent (using OpenAI) to invoke that tool.
@RGGH
基本情報
Rig MCP Server Example (April 7, 2025)
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"rig-mcp-server": {
"command": "npx",
"args": [
"@modelcontextprotocol/inspector",
"sse",
"http://127.0.0.1:3001/sse"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
This project demonstrates how to set up an MCP (Model Context Protocol) server and client using Server-Sent Events (SSE) for communication. It includes a simple tool that adds two numbers and integrates with the RIG agent for LLM prompting.
Clone the repository and run cargo run. In a separate terminal, start the MCP Inspector with npx @modelcontextprotocol/inspector sse http://127.0.0.1:3001/sse. Then open http://localhost:5173 in a web browser to view the Inspector UI.
ServerSseTransportAdd tool that adds two numbersIt sets up an MCP server with an SSE transport, registers a tool to add two numbers, connects an MCP client, and prompts a RIG agent (using OpenAI) to invoke that tool.
Rust with tokio, anyhow, serde_json, `mcp_core`, `mcp_core_macros`, and `rig`. The MCP Inspector requires Node.js to run `npx`.
Server-Sent Events (SSE) on port 3001, with the inspector proxy listening on port 3000 and a web UI on port 5173.
Yes, the RIG agent uses an OpenAI model, so a valid OpenAI API key must be set in the environment.
After starting the server and inspector, visit http://localhost:5173 to see the tool list and invoke the `Add` tool. The agent also runs a prompt `"Add 10 + 10"` and prints the response.

Give an agent a place to put large output. Instead of returning 40k characters of a build log into the conversation, it posts the file to DocBin and hands back one short URL. Docs render with syntax highlighting and mark

Know when your AI agents, cron jobs and CI pipelines fail, stall or go quiet. Your agent sets up monitors from a plain description, tells you what is broken and why, reads run history and traces, and writes notes on inci
Console, Backend and CLI to manage Capgo Instant update and Native build for Capacitor apps Build and upload to app store your mobile app

Turn Perfex CRM into an AI-ready workspace. This MCP server exposes your full REST API to Claude, ChatGPT and any AI agent, so leads, invoices and tasks are one prompt away.
OrangePro local-first CLI + MCP server for behavior mapping, grounded test generation, and dynamic proof.
コメント