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Query-to-action automation with Bright Data MCP & OpenAI GPT

Cyril Nicko GasparCyril Nicko Gaspar
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2/3/2026
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📌 AI Agent Template with Bright Data MCP Tool Integration

This template obtains all the possible tools from Bright Data MCP, process this through chatbot, then run any tool based on the user's query

❓ Problem It Solves

The problem that the MCP solves is the complexity and difficulty of traditional automation, where users need to have specific knowledge of APIs or interfaces to trigger backend processes. By allowing interaction through natural language, automatically classifying and routing queries, and managing context and memory effectively, MCP simplifies complex data operations, customer support, and workflow orchestration scenarios where inputs and responses change dynamically.

🧰 Pre-requisites

Before deploying this template, ensure you have:

  • An active n8n instance (self-hosted or cloud).
  • A valid OpenAI API key (or any AI models)
  • Access to Bright Data MCP API with credentials.
  • Basic familiarity with n8n workflows and nodes.

⚙️ Setup Instructions

  1. **Install the MCP Community Node in N8N

    • In your N8N self-hosted instance, go to Settings → Community Nodes.
    • Search and install n8n-nodes-mcp.

  2. Configure Credentials:

    • Add your OpenAI API key or any AI mdeols to the relevant nodes. If you want other AI model, please replace all associated nodes of OpenAI in the workflow
    • Set up Bright Data MCP client credentials in the installed community node (STDIO)
    • Obtain your API in Bright Data and put it in Environment field in the credentials window. It should be written as API_Key=<your api key from Bright Data> Screenshot 20250516 at 1.52.24 AM.png

🔄 Workflow Functionality (Summary)

  • User message triggers the workflow.
  • AI Classifier (OpenAI) interprets the intent and maps it to a tool from Bright Data MCP.
  • If no match is found, the user is notified.
  • If more information is needed, the AI requests it.
  • Memory preserves context for follow-up actions.
  • The tool is executed, and results are returned contextually to the user.

> 🧠 Optional memory buffer and chat memory manager nodes keep conversations context-aware across multiple messages.

🧩 Use Cases

  • Data Scraping Automation: Trigger scraping tasks via chat.
  • Lead Generation Bots: Use MCP tools to fetch, enrich, or validate data.
  • Customer Support Agents: Automatically classify and respond to queries with tool-backed answers.
  • Internal Workflow Agents: Let team members trigger backend jobs (e.g., reports, lookups) by chatting naturally.

🛠️ Customization

  • Tool Matching Logic: Modify the AI classifier prompt and schema to suit different APIs or services.
  • Memory Size and Retention: Adjust memory buffer size and filtering to fit your app’s complexity.
  • Tool Execution: Extend the "Execute the tool" sub-workflow to handle additional actions, fallback strategies, or logging.
  • Frontend Integration: Connect this with various platforms (e.g., WhatsApp, Slack, web chatbots) using the webhook.

✅ Summary

This template delivers a powerful no-code/low-code agent that turns chat into automation, combining AI intelligence with real-world tool execution. With minimal setup, you can build contextual, dynamic assistants that drive backend operations using natural language.

4077-query-to-action-automation-with-bright-data-mcp--openai-gpt

This n8n workflow demonstrates a sophisticated automation that leverages AI and external tools to process incoming chat messages, determine appropriate actions, and execute them. It acts as an intelligent agent, capable of understanding user queries and responding or taking action based on predefined tools and its own reasoning.

What it does

This workflow functions as an AI-powered conversational agent that can:

  1. Receive Chat Messages: It listens for incoming chat messages, acting as the primary trigger for the workflow.
  2. Manage Chat History: It maintains a memory of the conversation, allowing the AI to understand context and respond more coherently.
  3. Process with AI Agent: It uses an OpenAI GPT-powered AI Agent to analyze the chat message. This agent is configured with a specific persona and a set of tools it can utilize.
  4. Utilize Tools for Action: The AI Agent can decide to call an n8n workflow as a tool if the user's query requires an external action. This allows for dynamic integration with other n8n workflows or external services.
  5. Generate Responses: Based on the analysis and potential tool execution, the AI Agent generates a relevant response to the user.
  6. Conditional Logic (Placeholder): A conditional 'If' node is present, suggesting potential future expansion for branching logic based on the AI's output or other criteria. Currently, it's not actively connected to the main AI flow but serves as a structural element.
  7. Data Transformation (Placeholder): An 'Edit Fields (Set)' node is included, indicating a capability to transform or prepare data, likely for tool inputs or output formatting. Similar to the 'If' node, it's not directly integrated into the primary AI agent flow but stands as a potential point of integration.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance to import and execute the workflow.
  • OpenAI API Key: Credentials for OpenAI to use the Chat Model and AI Agent nodes.
  • LangChain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.
  • External n8n Workflows (Optional): If the "Call n8n Workflow Tool" is intended to execute specific actions, you will need the target n8n workflows configured and accessible.

Setup/Usage

  1. Import the Workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API credentials in the "OpenAI Chat Model" and "AI Agent" nodes.
  3. Customize AI Agent:
    • Review the configuration of the "AI Agent" node, including its persona and the tools it has access to.
    • If using the "Call n8n Workflow Tool", configure it to point to the specific n8n workflow you want the AI to be able to execute. This workflow will need an "Execute Workflow Trigger" node to receive input from the AI Agent.
  4. Activate Chat Trigger: Configure the "Chat Trigger" node to listen for messages from your desired chat platform (e.g., Slack, Telegram, Discord, etc.).
  5. Activate Workflow: Once configured, activate the workflow. It will then be ready to process incoming chat messages and respond intelligently.

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