Automating Web Research with AI Agents and Bright Data

Automating web research can significantly speed up tasks like competitive analysis, market research, and data gathering. By combining AI agents with powerful web interaction tools, you can build systems that efficiently extract and analyze information from the internet.

This guide will walk you through setting up and using Bright Data's tools, specifically their Multi-tool Calling Protocol (MCP) server, in conjunction with frameworks like the Google Agent Development Kit (ADK) to build AI-powered research agents.

Why Automate Web Research?

Manual web research is time-consuming and often inefficient. Websites change, data is unstructured, and dealing with anti-bot measures can be challenging. Automating this process allows you to:

• Gather large volumes of data quickly
• Monitor competitors' pricing and product information
• Identify market trends and content gaps
• Streamline data analysis workflows

The goal is to create agents that can autonomously navigate the web, extract relevant data, and present it in a usable format, freeing you to focus on analysis and strategy.

Setting Up Your Environment

To build these agents, you'll need a way for your AI client (like Claude Desktop or an ADK agent) to interact with the web. Bright Data provides the necessary infrastructure through its MCP server.

The MCP server acts as a bridge, allowing your AI agent to call various web interaction tools provided by Bright Data. These tools include capabilities for:

• Searching the web (search_engine)
• Scraping web pages (scrape_as_markdown, scrape_as_html)
• Extracting structured data from specific sites like Amazon, Walmart, eBay, and more (web_data_amazon_product, web_data_walmart_product, etc.)

Configuration Example

Here's an example of how you might configure it in Claude Desktop:

json
{
  "mcpServers": {
    "brightdata": {
      "command": "npx",
      "args": [
        "@brightdata/mcp"
      ],
      "env": {
        "API_TOKEN": "<insert-your-api-token-here>"
      }
    }
  }
}

In this configuration:

• "brightdata" is the name you give to this MCP server instance
• "command": "npx" indicates that the server is run using the npx command (requires Node.js)
• "args": ["@brightdata/mcp"] specifies the package to run
• "env": {"API_TOKEN": "..."} is crucial for authentication - replace with your actual Bright Data API token

You can find your API token in your Bright Data account settings, typically under User access or API keys.

Security Considerations

When working with data scraped from the web, it's important to prioritize security. Web content should always be treated as untrusted data. To prevent potential prompt injection attacks:

• Filter and validate all web data before feeding it into an LLM prompt
• Prefer structured data extraction using tools like web_data_amazon_product over simply scraping raw text (scrape_as_markdown or scrape_as_html) whenever possible
• Use Bright Data's security features including Web Unlocker and Browser Control for robust data acquisition

Structured data is easier to validate and less likely to contain malicious prompts.

Building a Research Agent: Price Comparison Example

Let's look at a practical example: building an agent to compare product prices across different online retailers.

Example: Divine Essence Essential Oils Comparison

Imagine you want to compare the price of Divine Essence essential oils between Opal Wellness Pharmacy (shop.opalwellness.ca) and Well.ca.

Step 1: Extract Data from Source 1 (Opal Wellness)

Your agent would use a Bright Data tool, perhaps scrape_as_markdown or a more specific web_data tool if available for this site, to extract the product list and prices from the Opal Wellness essential oils collection page.

The agent might extract data like this:

• Divine Essence - Calendula Extract Organic 30ml - $17.99
• Divine Essence - Arnica Extract Organic 30ml - $21.99
• Divine Essence - Turmeric (Curcuma) Organic 5ml - $11.99
• Divine Essence - Eczema and Dermatitis Organic Roll-on No1 15ml - $16.99
• Divine Essence - Tea Tree Organic 15ml - $14.99

Step 2: Search for Data on Source 2 (Well.ca)

For each product extracted from Opal Wellness, the agent would then use the search_engine tool to find the corresponding product on Well.ca. The search query would be formulated to specifically target the well.ca domain and include the product name, for example:

json
{
  "query": "site:well.ca \"Divine Essence Calendula Extract Organic 30ml\" price"
}

Step 3: Compile and Compare

After gathering the prices from both websites, the agent compiles the data into a comparison table:

The agent can then summarize the findings, noting which retailer is cheaper and the total savings. In this example, Well.ca was consistently cheaper, resulting in a total saving of $20.70 across these five products.

Using the Google Agent Development Kit (ADK)

The Google ADK provides a framework for building, testing, and managing AI agents. It offers a development UI (accessible typically at 127.0.0.1:8000/dev-ui/) that allows you to:

• Monitor agent execution
• View tool calls
• Inspect the agent's state and artifacts

When you integrate the Bright Data MCP server with the ADK, the ADK agent can directly call the Bright Data tools configured via the MCP. This simplifies the development process, as you don't need to build the web scraping logic from scratch.

Example ADK Integration

You could instruct an ADK agent to "Find me the top deals for TVs on walmart and best buy". The agent, using the configured Bright Data MCP server, would:

1. Call the search_engine tool with appropriate queries like "walmart tv deals" and "best buy tv deals"
2. The ADK UI would show these tool calls and their responses
3. The agent would then process the search results and summarize the findings
4. Present specific deals found on each site

Building Custom Applications

Beyond simple research tasks, you can leverage the combination of Bright Data MCP and frameworks like the Google ADK to build custom applications.

Slack App Example

You can create a Slack app (e.g., named "Bright Data Research Agent") that users can interact with directly within Slack. This app, powered by an ADK agent using the Bright Data MCP, could respond to user requests like:

"Scrape the latest products from shop.opalwellness.ca/collections/bath-body"

The Slack app's manifest (slack_app_manifest.json) defines its basic information and features:

json
{
  "display_information": {
    "name": "Bright Data MCP Server Overview",
    "description": "A Tester for Bright Data MCP Powered Agents",
    "background_color": "#000000",
    "long_description": "A comprehensive research agent powered by Bright Data's advanced"
  },
  "features": {
    "app_home": {
      "home_tab_enabled": true,
      "messages_tab_enabled": true,
      "messages_tab_read_only_enabled": false
    },
    "bot_user": {
      "display_name": "Bright Data Research Agent",
      "always_online": true
    },
    "assistant_view": {
    }
  }
}

Workflow Process

When a user sends a request in Slack:

1. The app triggers the underlying ADK agent
2. The agent uses the Bright Data MCP tools (like scrape_as_markdown) to fetch data from the specified URL
3. The agent potentially scrapes multiple pages of the website
4. Terminal output shows the scrape_as_markdown tool executing for different pages
5. Once data is collected and processed, it's formatted and displayed back to the user in the Slack conversation

This demonstrates how you can create powerful, integrated research tools that bring web data directly into your team's workflow.

Conclusion

Combining AI agents with robust web interaction tools like Bright Data's MCP server and development frameworks like the Google ADK offers a powerful approach to automating web research and data extraction.

Key Takeaways

• Bright Data's MCP server provides a wide range of tools for interacting with the web, including scraping, searching, and extracting structured data from popular sites
• Integration with AI frameworks like the Google ADK allows you to build sophisticated research agents that can autonomously perform complex web-based tasks
• Practical applications include competitive pricing intelligence, market trend analysis, and building custom data-gathering tools like Slack bots
• Security is paramount - always handle scraped data securely by validating and filtering it before use, especially when integrating with LLMs

By leveraging these tools and practices, you can build effective AI-powered research agents tailored to your specific needs, significantly enhancing efficiency and providing valuable insights for your business or research objectives.