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LLM SSE MCP Demo

@nlinhvu

リポジトリ

基本情報

カテゴリ

AI とエージェント

ライセンス

MIT

トランスポート

stdio

作者

nlinhvu

LLM SSE MCP Demo について

This project demonstrates the integration between LLM clients and MCP (Model Context Protocol) servers using Server-Sent Events (SSE) for real-time communication.

LLM SSE MCP Demo の設定

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": {
    "llm-sse-mcp-demo-2025": {
      "command": "docker",
      "args": [
        "compose",
        "up",
        "-d"
      ]
    }
  }
}

LLM SSE MCP Demo のツール

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。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is LLM SSE MCP Demo?

LLM SSE MCP Demo is a multi-application demo project that integrates LLM clients with MCP (Model Context Protocol) servers using Server-Sent Events (SSE) for real-time communication. It includes an OAuth2 authorization server, an MCP server exposing math and date/time tools, and a web-based LLM client supporting Anthropic Claude, OpenAI GPT, Google Gemini, and Ollama. It targets developers building LLM-powered applications with external tool calling and observability.

How to use LLM SSE MCP Demo?

Start the observability stack (Prometheus, Tempo, Loki, Grafana) with Docker Compose, then run each Spring Boot app via ./gradlew bootRun in order: authorization server (port 9000), MCP server (port 8080), LLM client (port 10101). Set environment variables for cloud LLM API keys as needed. Open a browser to http://localhost:10101 to send queries like math operations or time requests.

Key features of LLM SSE MCP Demo

  • OAuth 2.0 authorization for MCP services
  • Mathematical tools: addition and multiplication
  • Date/time tools: current time and alarm setting
  • SSE-based real-time client-server communication
  • Multi-provider LLM support (Claude, GPT, Gemini, Ollama)
  • Integrated observability with Grafana, Prometheus, Tempo, Loki

Use cases of LLM SSE MCP Demo

  • Building LLM applications that can perform calculations and time queries
  • Demonstrating MCP protocol tool discovery and execution with SSE
  • Exploring OAuth2 security for LLM-triggered tool calls
  • Testing multi-model LLM integration in a single chat interface
  • Learning observability setup for MCP-based Spring Boot services

よくある質問

1

What LLM models are supported?

Anthropic Claude (claude-sonnet-4-20250514), OpenAI (o4-mini-2025-04-16), Google Gemini (gemini-2.5-flash-preview-05-20), and Ollama (qwen3:8b locally).

2

What tools does the MCP server provide?

Four tools: `sumNumbers(int, int)` – adds two numbers; `multiplyNumbers(int, int)` – multiplies two numbers; `getCurrentDateTime()` – returns current date/time; `setAlarm(String)` – sets an alarm for an ISO-8601 time.

3

How do I start the demo?

Start the Docker Compose services for observability, then run each Spring Boot application in order: authorization server, MCP server, LLM client, using `./gradlew bootRun`. Access the chat at `http://localhost:10101` and Grafana at `http://localhost:3000`.

4

What are the prerequisites?

Java 17 or higher, Gradle, Docker and Docker Compose, API keys for cloud LLM providers (optional), and Ollama installed locally if using local models.

5

How do I access the web interface?

Open `http://localhost:10101` in a browser after starting the LLM client. For Grafana dashboards (Prometheus, Tempo, Loki), use `http://localhost:3000`.

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