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    "name": "Enhance Chat Responses with Real-Time Search via Bright Data MCP & Gemini AI",
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    "recentViews": 0,
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    "createdAt": "2025-04-28T23:46:16.740Z",
    "description": "### Disclaimer\nThis template is only available on n8n self-hosted as it's making use of the community node for MCP Client.\n\n![Enhance Chat Responses with RealTime Search Data.png](fileId:1224)\n\n### Who this is for?\n\nThe Chat Conversations with Bright Data MCP Search Engines & Google Gemini workflow is designed for users who need real-time, AI-enhanced conversations powered by live search engine results. \n\nThis workflow is tailored for:​\n\n1. **Data Analysts** - Who want live, search-based data fused with AI reasoning.\n\n2. **Marketing Researchers** - Seeking up-to-the-minute market or competitor insights via conversational AI.\n\n3. **Product Managers** - Exploring user needs, market trends, and competitor analysis in real time.\n\n4. **AI Developers** - Building dynamic applications that combine live search data with intelligent conversation agents.\n\n5. **Growth Hackers** - Who need fast, conversational research tools for campaign ideation, outreach, or content creation.\n\n### What problem is this workflow solving?\n\nTraditional chatbots and AI systems often rely on static, outdated data. \n\nThis workflow enables AI agents to fetch live search engine data and converse intelligently about it, making interactions dynamic, accurate, and highly contextual.\n\nThis workflow solves the major gaps of:\n\n1. **Outdated Knowledge**: Regular chatbots lack up-to-date information from live web searches.\n\n2. **Manual Search Fatigue**: Manually searching for information and interpreting it is time-consuming.\n\n3. **Context Bridging**: Connecting search results into meaningful, conversational replies requires human-level reasoning.\n\n### What this workflow does?\n\n1. Accepts a user's conversational query input.\n\n2. Triggers a search request to Bright Data’s MCP Search Engines API (Google, Bing, etc.) based on the query.\n\n3. Waits for the search task to complete.\n\n4. Retrieves real-time search results.\n\n5. Feeds the search results and original question into Google Gemini.\n\n6. Generates a human-like, contextually accurate AI response combining live information and conversational flow.\n\n7. Outputs the response back into a chat app.\n\n### Pre-conditions\n\n1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - [model-context-protocol](https://www.anthropic.com/news/model-context-protocol)\n2. You need to have the [Bright Data](https://brightdata.com/) account and do the necessary setup as mentioned in the **Setup** section below.\n3. You need to have the Google Gemini API Key. Visit [Google AI Studio](https://aistudio.google.com/)\n3. You need to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp)\n4. You need to install the [n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp)\n\n### Setup\n1. Please make sure to setup n8n locally with MCP Servers by navigating to [n8n-nodes-mcp](https://www.youtube.com/watch?v=NUb73ErUCsA)\n2. Please make sure to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) on your local machine. Also, do \"Account Setup\" as mentioned in the [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) URL.\n3. Sign up at [Bright Data](https://brightdata.com/).\n4. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.\n5. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy).\n6. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below.\n\n![MCPClientAccount.png](fileId:1222)\nMake sure to copy the Bright Data Web Unlocker **API Token** within the Environments textbox above as API_TOKEN=<your-token>.\n7. Update the **HTTP Request for Webhook Notification** node for sending the Webhook notification for chat responses.\n\n### How to customize this workflow to your needs\n\n1. **Change Search Engine**:\n- Add or Remove the Search Engine MCP tools based upon the Bright Data MCP Server updates.\n\n2. **Expand Outputs**:\n- Send AI chat responses to Slack, Discord, custom chat UIs, WhatsApp, or CRM systems.\n- Store conversation logs in a database (PostgreSQL, MongoDB, etc.) for future audits or training.",
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      },
      "name": "Enhance Chat Responses with Real-Time Search Data via Bright Data & Gemini AI",
      "tags": [
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          "name": "Engineering",
          "createdAt": "2025-04-09T01:31:00.558Z",
          "updatedAt": "2025-04-09T01:31:00.558Z"
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          "name": "Building Blocks",
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          "name": "AI",
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            "options": {
              "systemMessage": "You are a helpful assistant.\n\nUse MCP Search Engine assistant tools for Bright Data for Google, Bing or Yandex Search. \n\nImportant: Return the response to Chat and also perform the webhook notification of responses.\n\nUse the relevant tool in the order of execution. "
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            "modelName": "models/gemini-2.0-flash-exp"
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          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
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          "name": "MCP Client list all tools for Bright Data",
          "type": "n8n-nodes-mcp.mcpClient",
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          "parameters": {},
          "credentials": {
            "mcpClientApi": {
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          "name": "MCP Client Bright Data Search Tool",
          "type": "n8n-nodes-mcp.mcpClient",
          "position": [
            160,
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          ],
          "parameters": {
            "toolName": "={{ $('MCP Client list all tools for Bright Data').item.json.tools[0].name }}",
            "operation": "executeTool",
            "toolParameters": "={\n   \"query\": \"{{ $json.search_query }}\",\n   \"engine\": \"google\"\n} "
          },
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
          },
          "typeVersion": 1
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          "name": "Set search query",
          "type": "n8n-nodes-base.set",
          "position": [
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          "parameters": {
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            "assignments": {
              "assignments": [
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                  "name": "search_query",
                  "type": "string",
                  "value": "Bright Data"
                }
              ]
            }
          },
          "typeVersion": 3.4
        },
        {
          "id": "1dc4dabe-d651-4b43-b561-4528be14e578",
          "name": "Google Search Engine for Bright Data",
          "type": "n8n-nodes-mcp.mcpClientTool",
          "notes": "Scrape search results from Google, Bing or Yandex. Returns SERP results in markdown (URL, title, description)",
          "position": [
            240,
            540
          ],
          "parameters": {
            "toolName": "search_engine",
            "operation": "executeTool",
            "toolParameters": "={\n   \"query\": \"{{ $json.chatInput }}\",\n   \"engine\": \"google\"\n}"
          },
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
          },
          "notesInFlow": true,
          "typeVersion": 1
        },
        {
          "id": "029f5e0e-070f-47a7-8c77-2b59ca01ada4",
          "name": "Bing Search Engine for Bright Data",
          "type": "n8n-nodes-mcp.mcpClientTool",
          "notes": "Scrape search results from Google, Bing or Yandex. Returns SERP results in markdown (URL, title, description)",
          "position": [
            40,
            540
          ],
          "parameters": {
            "toolName": "search_engine",
            "operation": "executeTool",
            "toolParameters": "={\n   \"query\": \"{{ $json.chatInput }}\",\n   \"engine\": \"bing\"\n} "
          },
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
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          "notesInFlow": true,
          "typeVersion": 1
        },
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          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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          "parameters": {
            "color": 4,
            "width": 640,
            "height": 240,
            "content": "## Bright Data Search Engines"
          },
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        },
        {
          "id": "bb77ba7c-c70e-4912-96f6-4f63b966c7a9",
          "name": "Sticky Note1",
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          "position": [
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          "parameters": {
            "color": 3,
            "width": 460,
            "height": 260,
            "content": "## Bright Data Google Search"
          },
          "typeVersion": 1
        },
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          "id": "ecdd9f42-f56c-4bdb-b778-cd3b7545bb37",
          "name": "MCP Client List all tools",
          "type": "n8n-nodes-mcp.mcpClientTool",
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          "parameters": {},
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
          },
          "typeVersion": 1
        },
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          "name": "HTTP Request for Webhook Notification",
          "type": "@n8n/n8n-nodes-langchain.toolHttpRequest",
          "position": [
            500,
            240
          ],
          "parameters": {
            "url": "https://webhook.site/daf9d591-a130-4010-b1d3-0c66f8fcf467",
            "method": "POST",
            "sendBody": true,
            "parametersBody": {
              "values": [
                {
                  "name": "chat_response"
                }
              ]
            },
            "toolDescription": "Webhook notification for search responses"
          },
          "typeVersion": 1.1
        },
        {
          "id": "ae88bb19-170f-443f-b777-561cf2e3be25",
          "name": "Sticky Note2",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -100,
            -400
          ],
          "parameters": {
            "width": 440,
            "height": 120,
            "content": "## Disclaimer\nThis template is only available on n8n self-hosted as it's making use of the community node for MCP Client."
          },
          "typeVersion": 1
        },
        {
          "id": "80ac697d-2c4a-4f97-82aa-edcabbf7ef6f",
          "name": "Yandex Search Engine for Bright Data",
          "type": "n8n-nodes-mcp.mcpClientTool",
          "notes": "Scrape search results from Google, Bing or Yandex. Returns SERP results in markdown (URL, title, description)",
          "position": [
            460,
            540
          ],
          "parameters": {
            "toolName": "search_engine",
            "operation": "executeTool",
            "toolParameters": "={\n   \"query\": \"{{ $json.chatInput }}\",\n   \"engine\": \"yandex\"\n}"
          },
          "credentials": {
            "mcpClientApi": {
              "id": "credential-id",
              "name": "mcpClientApi Credential"
            }
          },
          "notesInFlow": true,
          "typeVersion": 1
        },
        {
          "id": "dfb2117d-782f-44d9-baca-1ee4b0fef863",
          "name": "Sticky Note3",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -940,
            -40
          ],
          "parameters": {
            "color": 5,
            "width": 400,
            "height": 220,
            "content": "## Note\nUse Bright Data MCP Search Engine assistant tools to perform Google, Bing or Yandex Search.\n\nThe AI Agent will make use of suitable search engine-based tools, returns the response to Chat and also performs the Webhook notification call for sending the AI responses via the MCP Client tools.\n\nSource - https://github.com/luminati-io/brightdata-mcp"
          },
          "typeVersion": 1
        },
        {
          "id": "694b3381-8ebe-4afb-be93-019715c0c2cf",
          "name": "Sticky Note4",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -440,
            460
          ],
          "parameters": {
            "width": 300,
            "height": 180,
            "content": "## LLM Usage\nGoogle Gemini is employed by the AI agent to understand and interpret user queries. Based on this interpretation, the agent initiates a call to the appropriate MCP client to perform the required web search task."
          },
          "typeVersion": 1
        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
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      "versionId": "2382b23d-fd06-4f10-bcbd-f09a944a1c8d",
      "connections": {
        "Simple Memory": {
          "ai_memory": [
            [
              {
                "node": "AI Agent",
                "type": "ai_memory",
                "index": 0
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            ]
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        },
        "Set search query": {
          "main": [
            [
              {
                "node": "MCP Client Bright Data Search Tool",
                "type": "main",
                "index": 0
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            ]
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        },
        "Google Gemini Chat Model": {
          "ai_languageModel": [
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              {
                "node": "AI Agent",
                "type": "ai_languageModel",
                "index": 0
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            ]
          ]
        },
        "MCP Client List all tools": {
          "ai_tool": [
            [
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                "node": "AI Agent",
                "type": "ai_tool",
                "index": 0
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        "When chat message received": {
          "main": [
            [
              {
                "node": "AI Agent",
                "type": "main",
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        "Bing Search Engine for Bright Data": {
          "ai_tool": [
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                "index": 0
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        "Google Search Engine for Bright Data": {
          "ai_tool": [
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                "index": 0
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        },
        "Yandex Search Engine for Bright Data": {
          "ai_tool": [
            [
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                "node": "AI Agent",
                "type": "ai_tool",
                "index": 0
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        "HTTP Request for Webhook Notification": {
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              {
                "node": "AI Agent",
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        "MCP Client list all tools for Bright Data": {
          "main": [
            [
              {
                "node": "Set search query",
                "type": "main",
                "index": 0
              }
            ]
          ]
        }
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    "status": "published",
    "user": {
      "name": "Ranjan Dailata",
      "username": "ranjancse",
      "bio": "A Professional based out of India specialized in handling AI-powered automations. Contact me at ranjancse@gmail.com ",
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}