メールニュースMCPテンプレート
上級
これはAI Chatbot, Multimodal AI分野の自動化ワークフローで、18個のノードを含みます。主にGmailTool, PerplexityTool, Agent, McpTrigger, TavilyToolなどのノードを使用。 メール案内作成とニュースリサーチアシスタント - OpenAI、Gmail、Tavily、Perplexityの統合
前提条件
- •Googleアカウント + Gmail API認証情報
- •OpenAI API Key
使用ノード (18)
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"id": "TgpCq3JAieEaFdGJ",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Email News MCP Template",
"tags": [],
"nodes": [
{
"id": "0606f766-255e-469c-8e6c-5751537ed3ab",
"name": "AIエージェント",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
192,
-160
],
"parameters": {
"options": {
"systemMessage": "You are a helpful email assistant.\n\n##Tool\nUse attached Email MCP Tool for emails when asked\n\nUse attached Email MCP Tool for "
}
},
"typeVersion": 2.2
},
{
"id": "225b0350-6eae-45fc-a158-da9961b8aafe",
"name": "チャットメッセージ受信時",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
0,
-160
],
"parameters": {
"options": {}
},
"typeVersion": 1.3
},
{
"id": "80fcfcad-1310-4cf2-a4df-bf6746339cfd",
"name": "OpenAIチャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
48,
48
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"typeVersion": 1.2
},
{
"id": "7e3db391-7ede-4e92-9593-7a1288938d80",
"name": "シンプルメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
224,
48
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "1b9a577c-3401-4081-be39-d5051922df38",
"name": "Gmailでメッセージ送信",
"type": "n8n-nodes-base.gmailTool",
"position": [
-144,
480
],
"parameters": {
"sendTo": "<<<REPLACE_WITH_EMAIL>>>",
"message": "<<<REPLACE_WITH_MESSAGE>>>",
"options": {},
"subject": "<<<REPLACE_WITH_SUBJECT>>>"
},
"typeVersion": 2.1
},
{
"id": "fa6ae7d8-3d4d-4bd0-a4f9-d1d295f5f14b",
"name": "Gmail1でメッセージ送信",
"type": "n8n-nodes-base.gmailTool",
"position": [
64,
480
],
"parameters": {
"sendTo": "<<<REPLACE_WITH_EMAIL>>>",
"message": "<<<REPLACE_WITH_MESSAGE>>>",
"options": {},
"subject": "<<<REPLACE_WITH_SUBJECT>>>"
},
"typeVersion": 2.1
},
{
"id": "252988a9-b546-4e1a-9d6f-338618b5781b",
"name": "Gmail2でメッセージ送信",
"type": "n8n-nodes-base.gmailTool",
"position": [
256,
480
],
"parameters": {
"sendTo": "<<<REPLACE_WITH_EMAIL>>>",
"message": "<<<REPLACE_WITH_MESSAGE>>>",
"options": {},
"subject": "<<<REPLACE_WITH_SUBJECT>>>"
},
"typeVersion": 2.1
},
{
"id": "722718e7-8a84-44b4-98e3-a6eb53902a7c",
"name": "Tavilyで検索",
"type": "@tavily/n8n-nodes-tavily.tavilyTool",
"position": [
512,
480
],
"parameters": {
"query": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Query', ``, 'string') }}",
"options": {}
},
"typeVersion": 1
},
{
"id": "0bba3a97-e1ee-46f5-abec-7713d6ff2948",
"name": "Perplexityでモデルにメッセージ送信",
"type": "n8n-nodes-base.perplexityTool",
"position": [
688,
480
],
"parameters": {
"options": {},
"messages": {
"message": [
{
"content": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('message0_Text', ``, 'string') }}"
}
]
},
"simplify": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Simplify_Output', ``, 'boolean') }}",
"requestOptions": {}
},
"typeVersion": 1
},
{
"id": "60b275c9-9e2f-4e3c-bc11-2477fe0bc951",
"name": "ニュースMCPサーバー",
"type": "@n8n/n8n-nodes-langchain.mcpTrigger",
"position": [
544,
256
],
"parameters": {
"path": "<<<REPLACE_WITH_PATH>>>"
},
"typeVersion": 2
},
{
"id": "946b0a9d-590f-4633-ac98-ce983bbb205f",
"name": "EメールMCPサーバー",
"type": "@n8n/n8n-nodes-langchain.mcpTrigger",
"position": [
-96,
256
],
"parameters": {
"path": "<<<REPLACE_WITH_PATH>>>"
},
"typeVersion": 2
},
{
"id": "34bff09d-95d1-446f-88cb-1c664d1ad754",
"name": "EメールMCPクライアント",
"type": "@n8n/n8n-nodes-langchain.mcpClientTool",
"position": [
544,
48
],
"parameters": {
"endpointUrl": "<<<REPLACE_WITH_ENDPOINT_URL>>>",
"serverTransport": "httpStreamable"
},
"typeVersion": 1.1
},
{
"id": "57587695-df6b-461d-8596-6561ce295f79",
"name": "ニュースMCPクライアント",
"type": "@n8n/n8n-nodes-langchain.mcpClientTool",
"position": [
384,
48
],
"parameters": {
"endpointUrl": "<<<REPLACE_WITH_ENDPOINT_URL>>>",
"serverTransport": "httpStreamable"
},
"typeVersion": 1.1
},
{
"id": "2e931983-39af-4b1d-9a16-e30cd536ff0b",
"name": "Tavily1で検索",
"type": "@tavily/n8n-nodes-tavily.tavilyTool",
"position": [
848,
480
],
"parameters": {
"query": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Query', ``, 'string') }}",
"options": {}
},
"typeVersion": 1
},
{
"id": "c8fc2868-c029-454f-b47c-6cf2a4f2fb7c",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1024,
-432
],
"parameters": {
"width": 736,
"height": 1808,
"content": "AI Agent MCP for Email & News Research \n\nBuild a chat-first MCP-powered research and outreach agent. This workflow lets you ask questions in an n8n chat, then the agent researches news (via Tavily + Perplexity through an MCP server) and drafts emails (via Gmail through a separate MCP server). It uses OpenAI for reasoning and short-term memory for coherent, multi‑turn conversations.\n\nWatch build along videos for workflows like these on: www.youtube.com/@automatewithmarc\n\nWhat this template does\n\nChat-native trigger: Start a conversation and ask for research or an email draft.\n\nMCP client tools: The agent talks to two MCP servers — one for Email work, one for News research.\n\nNews research stack: Uses Tavily (search) and Perplexity (LLM retrieval/answers) behind a News MCP server.\n\nEmail stack: Uses Gmail Tool to generate and send messages via an Email MCP server.\n\nReasoning + memory: OpenAI Chat Model + Simple Memory for context-aware, multi-step outputs.\n\nHow it works (node map)\n\nWhen chat message received → collects your prompt and routes it to the agent.\n\nAI Agent (system prompt = “helpful email assistant”) → orchestrates tools via MCP Clients.\n\nOpenAI Chat Model → reasoning/planning for research or email drafting.\n\nSimple Memory → keeps recent chat context for follow-ups.\n\nNews MCP Server exposes:\n\nTavily Tool (Search) and Perplexity Tool (Ask) for up-to-date findings.\n\nEmail MCP Server exposes:\n\nGmail Tool (To, Subject, Message via AI fields) to send or draft emails.\n\nThe MCP Clients (News/Email) plug into the Agent, so your single chat prompt can research and then draft/send emails in one flow.\n\nRequirements\n\nn8n (Cloud or self‑hosted)\n\nOpenAI API key for the Chat Model (set on the node)\n\nTavily, Perplexity, and Gmail credentials (connected on their respective tool nodes)\n\nPublicly reachable MCP Server endpoints (provided in the MCP Client nodes)\n\nSetup (quick start)\n\nImport the template and open it in the editor.\n\nConnect credentials on: OpenAI, Tavily, Perplexity, and Gmail tool nodes.\n\nConfirm MCP endpoints in both MCP Client nodes (News/Email) and leave transport as httpStreamable unless you have special requirements.\n\nRun the workflow. In chat, try:\n\n“Find today’s top stories on Kubernetes security and draft an intro email to Acme.”\n\n“Summarize the latest AI infra trends and email a 3‑bullet update to my team.”\n\nInputs & outputs\n\nInput: Natural-language prompt via chat trigger.\n\nTools used: News MCP (Tavily + Perplexity), Email MCP (Gmail).\n\nOutput: A researched summary and/or a drafted/sent email, returned in the chat and executed via Gmail when requested.\n\nWhy teams will love it\n\nOne prompt → research + outreach: No tab‑hopping between tools.\n\nUp-to-date answers: Pulls current info through Tavily/Perplexity.\n\nEmail finalization: Converts findings into send-ready drafts via Gmail.\n\nContext-aware: Memory keeps threads coherent across follow-ups.\n\nPro tips\n\nUse clear verbs in your prompt: “Research X, then email Y with Z takeaways.”\n\nFor safer runs, point Gmail to a test inbox first (or disable send and only draft).\n\nAdd guardrails in the Agent’s system message to match your voice/tone."
},
"typeVersion": 1
},
{
"id": "226bc7c3-d026-4dea-adec-1d8fc5a5481b",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-144,
-304
],
"parameters": {
"color": 5,
"width": 928,
"height": 512,
"content": "Agent & MCP Client"
},
"typeVersion": 1
},
{
"id": "4d9280da-af9b-4eab-be1a-9c25a6258022",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-256,
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],
"parameters": {
"color": 6,
"width": 672,
"height": 512,
"content": "Email MCP Server"
},
"typeVersion": 1
},
{
"id": "f55f5515-090b-4c3d-9e60-49e0588292a4",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
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224
],
"parameters": {
"color": 7,
"width": 672,
"height": 512,
"content": "News Research MCP Server"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"connections": {
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},
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"1b9a577c-3401-4081-be39-d5051922df38": {
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"225b0350-6eae-45fc-a158-da9961b8aafe": {
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}
}よくある質問
このワークフローの使い方は?
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか?
上級 - AIチャットボット, マルチモーダルAI
有料ですか?
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス(OpenAI APIなど)は別途料金が発生する場合があります。
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ワークフロー情報
難易度
上級
ノード数18
カテゴリー2
ノードタイプ10
作成者
Automate With Marc
@marconiAutomating Start-Up and Business processes. Helping non-techies understand and leverage Agentic AI with easy to understand step-by-step tutorials. Check out my educational content: https://www.youtube.com/@Automatewithmarc
外部リンク
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