E-Mail-News MCP-Vorlage

Experte

Dies ist ein AI Chatbot, Multimodal AI-Bereich Automatisierungsworkflow mit 18 Nodes. Hauptsächlich werden GmailTool, PerplexityTool, Agent, McpTrigger, TavilyTool und andere Nodes verwendet. E-Mail-Entwurf und Nachrichtenforschungsassistent - Integriert OpenAI, Gmail, Tavily und Perplexity

Voraussetzungen
  • Google-Konto + Gmail API-Anmeldedaten
  • OpenAI API Key
Workflow-Vorschau
Visualisierung der Node-Verbindungen, mit Zoom und Pan
Workflow exportieren
Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n
{
  "id": "TgpCq3JAieEaFdGJ",
  "meta": {
    "templateCredsSetupCompleted": true
  },
  "name": "Email News MCP Template",
  "tags": [],
  "nodes": [
    {
      "id": "0606f766-255e-469c-8e6c-5751537ed3ab",
      "name": "KI-Agent",
      "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": "Wenn Chat-Nachricht empfangen",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        0,
        -160
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.3
    },
    {
      "id": "80fcfcad-1310-4cf2-a4df-bf6746339cfd",
      "name": "OpenAI Chat-Modell",
      "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": "Einfacher Speicher",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        224,
        48
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "1b9a577c-3401-4081-be39-d5051922df38",
      "name": "Nachricht in Gmail senden",
      "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": "Nachricht in Gmail1 senden",
      "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": "Nachricht in Gmail2 senden",
      "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": "In Tavily suchen",
      "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": "Modell in Perplexity benachrichtigen",
      "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": "News MCP Server",
      "type": "@n8n/n8n-nodes-langchain.mcpTrigger",
      "position": [
        544,
        256
      ],
      "parameters": {
        "path": "<<<REPLACE_WITH_PATH>>>"
      },
      "typeVersion": 2
    },
    {
      "id": "946b0a9d-590f-4633-ac98-ce983bbb205f",
      "name": "E-Mail MCP Server",
      "type": "@n8n/n8n-nodes-langchain.mcpTrigger",
      "position": [
        -96,
        256
      ],
      "parameters": {
        "path": "<<<REPLACE_WITH_PATH>>>"
      },
      "typeVersion": 2
    },
    {
      "id": "34bff09d-95d1-446f-88cb-1c664d1ad754",
      "name": "E-Mail MCP Client",
      "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": "News MCP Client",
      "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": "In Tavily1 suchen",
      "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": "Haftnotiz",
      "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": "Haftnotiz1",
      "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": "Haftnotiz2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -256,
        224
      ],
      "parameters": {
        "color": 6,
        "width": 672,
        "height": 512,
        "content": "Email MCP Server"
      },
      "typeVersion": 1
    },
    {
      "id": "f55f5515-090b-4c3d-9e60-49e0588292a4",
      "name": "Haftnotiz3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        432,
        224
      ],
      "parameters": {
        "color": 7,
        "width": 672,
        "height": 512,
        "content": "News Research MCP Server"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "connections": {
    "7e3db391-7ede-4e92-9593-7a1288938d80": {
      "ai_memory": [
        [
          {
            "node": "0606f766-255e-469c-8e6c-5751537ed3ab",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "57587695-df6b-461d-8596-6561ce295f79": {
      "ai_tool": [
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            "type": "ai_tool",
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          }
        ]
      ]
    },
    "Email MCP Client": {
      "ai_tool": [
        [
          {
            "node": "0606f766-255e-469c-8e6c-5751537ed3ab",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "722718e7-8a84-44b4-98e3-a6eb53902a7c": {
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            "type": "ai_tool",
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          }
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    },
    "80fcfcad-1310-4cf2-a4df-bf6746339cfd": {
      "ai_languageModel": [
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          {
            "node": "0606f766-255e-469c-8e6c-5751537ed3ab",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "2e931983-39af-4b1d-9a16-e30cd536ff0b": {
      "ai_tool": [
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    },
    "1b9a577c-3401-4081-be39-d5051922df38": {
      "ai_tool": [
        [
          {
            "node": "Email MCP Server",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "fa6ae7d8-3d4d-4bd0-a4f9-d1d295f5f14b": {
      "ai_tool": [
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          {
            "node": "Email MCP Server",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "252988a9-b546-4e1a-9d6f-338618b5781b": {
      "ai_tool": [
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          }
        ]
      ]
    },
    "225b0350-6eae-45fc-a158-da9961b8aafe": {
      "main": [
        [
          {
            "node": "0606f766-255e-469c-8e6c-5751537ed3ab",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "0bba3a97-e1ee-46f5-abec-7713d6ff2948": {
      "ai_tool": [
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            "type": "ai_tool",
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          }
        ]
      ]
    }
  }
}
Häufig gestellte Fragen

Wie verwende ich diesen Workflow?

Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.

Für welche Szenarien ist dieser Workflow geeignet?

Experte - KI-Chatbot, Multimodales KI

Ist es kostenpflichtig?

Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.

Workflow-Informationen
Schwierigkeitsgrad
Experte
Anzahl der Nodes18
Kategorie2
Node-Typen10
Schwierigkeitsbeschreibung

Für fortgeschrittene Benutzer, komplexe Workflows mit 16+ Nodes

Autor
Automate With Marc

Automate With Marc

@marconi

Automating 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

Externe Links
Auf n8n.io ansehen

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Kategorien

Kategorien: 34