MCP + OpenAI Agents SDK: How to Build a Powerful AI Agent

When OpenAI dropped their Agents SDK a few weeks ago, devs everywhere got hyped—and for good reason! Finally, a straightforward way to build goal-driven AI agents that can actually reason and get work done 🤯. Agents SDK get work done But almost in the same breath, MCP exploded onto the scene, quickly becoming the next hot trend in this wild AI landscape. So, why not smash these two powerhouses together?! That's precisely what we're about to do right here 🔥. MCP In this guide, we're going to forge a formidable AI agent leveraging the OpenAI Agents SDK and plugging it directly into the real-world tools provided by an MCP server. Get ready to supercharge your AI! we're going to forge a formidable AI agent A Quick Intro to MCP + OpenAI Agents SDK Integration OpenAI Agents SDK is one of the most popular Python toolkits for crafting intelligent AI agents, letting them reason and act. But here's the kicker: without external info, they're often stuck in a training data bubble. That's where MCP (Model Context Protocol) swoops in! Think of MCP as the universal USB-C port for your AI, giving it a standardized way to plug into any external data source or tool–local files, APIs, databases, you name it. 🔌 any Integrating MCP with the OpenAI Agents SDK means your agents get real-time, dynamic context, turning them from smart-but-isolated bots into powerhouse data explorers and action-takers! 🧠 Building an AI Agent with MCP Integration Using OpenAI Agents SDK Alright, enough theory. You get it—MCP supercharges AI agents like they’re Iron Man in his iconic metal suit. 🦾 Iron Man In this step-by-step section, we’re going to use the OpenAI Agents SDK—yep, the new Python SDK from OpenAI—for building an AI agent with MCP integration. Why OpenAI Agents SDK and not another library? Because it has native MCP support, which means your agent can go from smart to superpowered in seconds. native MCP support superpowered Now, small catch: OpenAI’s API isn’t free. 😅 So in this guide, we’ll run it using Gemini as the LLM engine instead (don’t worry—we’ll show how to swap in OpenAI or any other model too 🔁). we’ll run it using Gemini as the LLM engine instead ❓ But wait—what MCP server are we using? ❓ Great question! To get started fast, we’re plugging into Bright Data MCP Server—an MCP server built for web automation and data tasks. It lets your AI agent search, browse, take action, and extract real-time data from the Web—with built-in stealth to dodge CAPTCHAs and anti-bot traps. Bright Data MCP Server anti-bot traps Specifically, below are all the tools supported by the Bright Data MCP server 🛠️: Tool Description search_engine Scrape SERP data (from Google, Bing, Yandex, and more) scrape_as_markdown Scrape a URL and return content in clean Markdown scrape_as_html Scrape a URL and return full HTML content session_stats View tool usage during the current session web_data_amazon_product Get Amazon product info by URL web_data_amazon_product_reviews Fetch Amazon reviews for a product web_data_linkedin_person_profile Grab LinkedIn profile data web_data_linkedin_company_profile Fetch LinkedIn company data web_data_zoominfo_company_profile Get ZoomInfo company profile web_data_instagram_profiles Fetch Instagram profile details web_data_instagram_posts Get Instagram post data web_data_instagram_reels Grab Instagram reel data web_data_instagram_comments Fetch comments from an Instagram post web_data_facebook_posts Extract Facebook post info web_data_facebook_marketplace_listings Get Facebook Marketplace listings web_data_facebook_company_reviews Scrape Facebook business reviews web_data_x_posts Fetch data from X (formerly Twitter) posts web_data_zillow_properties_listing Get Zillow property listings web_data_booking_hotel_listings Fetch Booking.com hotel listings web_data_youtube_videos Extract YouTube video metadata scraping_browser_navigate Navigate to a new URL in a virtual browser scraping_browser_go_back Go back one page in the browser scraping_browser_go_forward Go forward one page in the browser scraping_browser_click Click a page element (needs selector) scraping_browser_links Get all links and selectors on the page scraping_browser_type Type into a form or input scraping_browser_wait_for Wait for an element to appear scraping_browser_screenshot Capture a screenshot of the current page scraping_browser_get_html Get raw HTML from the page scraping_browser_get_text Extract text content from the page Tool Description search_engine Scrape SERP data (from Google, Bing, Yandex, and more) scrape_as_markdown Scrape a URL and return content in clean Markdown scrape_as_html Scrape a URL and return full HTML content session_stats View tool usage during the current session web_data_amazon_product Get Amazon product info by URL web_data_amazon_product_reviews Fetch Amazon reviews for a product web_data_linkedin_person_profile Grab LinkedIn profile data web_data_linkedin_company_profile Fetch LinkedIn company data web_data_zoominfo_company_profile Get ZoomInfo company profile web_data_instagram_profiles Fetch Instagram profile details web_data_instagram_posts Get Instagram post data web_data_instagram_reels Grab Instagram reel data web_data_instagram_comments Fetch comments from an Instagram post web_data_facebook_posts Extract Facebook post info web_data_facebook_marketplace_listings Get Facebook Marketplace listings web_data_facebook_company_reviews Scrape Facebook business reviews web_data_x_posts Fetch data from X (formerly Twitter) posts web_data_zillow_properties_listing Get Zillow property listings web_data_booking_hotel_listings Fetch Booking.com hotel listings web_data_youtube_videos Extract YouTube video metadata scraping_browser_navigate Navigate to a new URL in a virtual browser scraping_browser_go_back Go back one page in the browser scraping_browser_go_forward Go forward one page in the browser scraping_browser_click Click a page element (needs selector) scraping_browser_links Get all links and selectors on the page scraping_browser_type Type into a form or input scraping_browser_wait_for Wait for an element to appear scraping_browser_screenshot Capture a screenshot of the current page scraping_browser_get_html Get raw HTML from the page scraping_browser_get_text Extract text content from the page Tool Description Tool Tool Tool Description Description Description search_engine Scrape SERP data (from Google, Bing, Yandex, and more) search_engine search_engine search_engine Scrape SERP data (from Google, Bing, Yandex, and more) Scrape SERP data (from Google, Bing, Yandex, and more) scrape_as_markdown Scrape a URL and return content in clean Markdown scrape_as_markdown scrape_as_markdown scrape_as_markdown Scrape a URL and return content in clean Markdown Scrape a URL and return content in clean Markdown scrape_as_html Scrape a URL and return full HTML content scrape_as_html scrape_as_html scrape_as_html Scrape a URL and return full HTML content Scrape a URL and return full HTML content session_stats View tool usage during the current session session_stats session_stats session_stats View tool usage during the current session View tool usage during the current session web_data_amazon_product Get Amazon product info by URL web_data_amazon_product web_data_amazon_product web_data_amazon_product Get Amazon product info by URL Get Amazon product info by URL web_data_amazon_product_reviews Fetch Amazon reviews for a product web_data_amazon_product_reviews web_data_amazon_product_reviews web_data_amazon_product_reviews Fetch Amazon reviews for a product Fetch Amazon reviews for a product web_data_linkedin_person_profile Grab LinkedIn profile data web_data_linkedin_person_profile web_data_linkedin_person_profile web_data_linkedin_person_profile Grab LinkedIn profile data Grab LinkedIn profile data web_data_linkedin_company_profile Fetch LinkedIn company data web_data_linkedin_company_profile web_data_linkedin_company_profile web_data_linkedin_company_profile Fetch LinkedIn company data Fetch LinkedIn company data web_data_zoominfo_company_profile Get ZoomInfo company profile web_data_zoominfo_company_profile web_data_zoominfo_company_profile web_data_zoominfo_company_profile Get ZoomInfo company profile Get ZoomInfo company profile web_data_instagram_profiles Fetch Instagram profile details web_data_instagram_profiles web_data_instagram_profiles web_data_instagram_profiles Fetch Instagram profile details Fetch Instagram profile details web_data_instagram_posts Get Instagram post data web_data_instagram_posts web_data_instagram_posts web_data_instagram_posts Get Instagram post data Get Instagram post data web_data_instagram_reels Grab Instagram reel data web_data_instagram_reels web_data_instagram_reels web_data_instagram_reels Grab Instagram reel data Grab Instagram reel data web_data_instagram_comments Fetch comments from an Instagram post web_data_instagram_comments web_data_instagram_comments web_data_instagram_comments Fetch comments from an Instagram post Fetch comments from an Instagram post web_data_facebook_posts Extract Facebook post info web_data_facebook_posts web_data_facebook_posts web_data_facebook_posts Extract Facebook post info Extract Facebook post info web_data_facebook_marketplace_listings Get Facebook Marketplace listings web_data_facebook_marketplace_listings web_data_facebook_marketplace_listings web_data_facebook_marketplace_listings Get Facebook Marketplace listings Get Facebook Marketplace listings web_data_facebook_company_reviews Scrape Facebook business reviews web_data_facebook_company_reviews web_data_facebook_company_reviews web_data_facebook_company_reviews Scrape Facebook business reviews Scrape Facebook business reviews web_data_x_posts Fetch data from X (formerly Twitter) posts web_data_x_posts web_data_x_posts web_data_x_posts Fetch data from X (formerly Twitter) posts Fetch data from X (formerly Twitter) posts web_data_zillow_properties_listing Get Zillow property listings web_data_zillow_properties_listing web_data_zillow_properties_listing web_data_zillow_properties_listing Get Zillow property listings Get Zillow property listings web_data_booking_hotel_listings Fetch Booking.com hotel listings web_data_booking_hotel_listings web_data_booking_hotel_listings web_data_booking_hotel_listings Fetch Booking.com hotel listings Fetch Booking.com hotel listings Booking.com web_data_youtube_videos Extract YouTube video metadata web_data_youtube_videos web_data_youtube_videos web_data_youtube_videos Extract YouTube video metadata Extract YouTube video metadata scraping_browser_navigate Navigate to a new URL in a virtual browser scraping_browser_navigate scraping_browser_navigate scraping_browser_navigate Navigate to a new URL in a virtual browser Navigate to a new URL in a virtual browser scraping_browser_go_back Go back one page in the browser scraping_browser_go_back scraping_browser_go_back scraping_browser_go_back Go back one page in the browser Go back one page in the browser scraping_browser_go_forward Go forward one page in the browser scraping_browser_go_forward scraping_browser_go_forward scraping_browser_go_forward Go forward one page in the browser Go forward one page in the browser scraping_browser_click Click a page element (needs selector) scraping_browser_click scraping_browser_click scraping_browser_click Click a page element (needs selector) Click a page element (needs selector) scraping_browser_links Get all links and selectors on the page scraping_browser_links scraping_browser_links scraping_browser_links Get all links and selectors on the page Get all links and selectors on the page scraping_browser_type Type into a form or input scraping_browser_type scraping_browser_type scraping_browser_type Type into a form or input Type into a form or input scraping_browser_wait_for Wait for an element to appear scraping_browser_wait_for scraping_browser_wait_for scraping_browser_wait_for Wait for an element to appear Wait for an element to appear scraping_browser_screenshot Capture a screenshot of the current page scraping_browser_screenshot scraping_browser_screenshot scraping_browser_screenshot Capture a screenshot of the current page Capture a screenshot of the current page scraping_browser_get_html Get raw HTML from the page scraping_browser_get_html scraping_browser_get_html scraping_browser_get_html Get raw HTML from the page Get raw HTML from the page scraping_browser_get_text Extract text content from the page scraping_browser_get_text scraping_browser_get_text scraping_browser_get_text Extract text content from the page Extract text content from the page Ready to roll? See how to get your AI Agent talking to the MCP server for unlimited power! ⚡ ⚡ Step #1: Project Setup To follow along with this tutorial section, make sure you’ve got the following prerequisites in place: Python 3.10+ installed locally 🐍 Node.js installed locally 🟩 An API key from a supported LLM provider for the OpenAI Agents SDK (we recommend Gemini for this one) 🔑 Python 3.10+ installed locally 🐍 Node.js installed locally 🟩 An API key from a supported LLM provider for the OpenAI Agents SDK (we recommend Gemini for this one) 🔑 Gemini We’ll skip the boring setup fluff and assume you’ve already created a Python project with this structure: /openai-agents-mcp ├── venv/ ├── .env └── agent.py /openai-agents-mcp ├── venv/ ├── .env └── agent.py Now, activate your virtual environment (venv/) and install the required packages: venv/ pip install openai-agents python-dotenv pip install openai-agents python-dotenv Where: openai-agents is the OpenAI Agents Python SDK—your gateway to AI agent magic 🪄 python-dotenv is for reading values from a .env file 🔐 openai-agents is the OpenAI Agents Python SDK—your gateway to AI agent magic 🪄 openai-agents OpenAI Agents Python SDK python-dotenv is for reading values from a .env file 🔐 python-dotenv .env Next, open agent.py and wire up your imports. Time to read in those environment variables and prep for agent launch: agent.py import asyncio from dotenv import load_dotenv import os from agents import ( Runner, Agent, OpenAIChatCompletionsModel, set_default_openai_client, set_tracing_disabled ) from openai import AsyncOpenAI from agents.mcp import MCPServerStdio # Load environment variables from the .env file load_dotenv() async def run(): # AI Agent logic goes here... pass if __name__ == "__main__": asyncio.run(run()) import asyncio from dotenv import load_dotenv import os from agents import ( Runner, Agent, OpenAIChatCompletionsModel, set_default_openai_client, set_tracing_disabled ) from openai import AsyncOpenAI from agents.mcp import MCPServerStdio # Load environment variables from the .env file load_dotenv() async def run(): # AI Agent logic goes here... pass if __name__ == "__main__": asyncio.run(run()) 💣 Boom! You're up and running with the basics. Step #2: Get Started With the Bright Data MCP Server So, you want your AI Agent to talk to an MCP server? 🥁 🥁 🥁 want your AI Agent to talk to an MCP server? Well, guess what—you’ll need an MCP server running first (yes, Captain Obvious reporting for duty! 🫡) Captain Obvious As mentioned earlier, we’ll be using the Bright Data MCP Server for this part. First, grab your API key and initialize the server as per the official Bright Data MCP server docs. Bright Data MCP Server grab your API key and initialize the server as per the official Bright Data MCP server docs Once you're ready, launch the server with this command: npx -y @brightdata/mcp npx -y @brightdata/mcp If everything’s configured correctly, you should see output similar to this: Checking for required zones... Required zone "mcp_unlocker" already exists Starting server... Checking for required zones... Required zone "mcp_unlocker" already exists Starting server... Nice! 🏄 Now, set up the environment variables that the agent will use to connect to the Bright Data MCP server. Open your .env file and add these two lines: .env BRIGHT_DATA_API_TOKEN=" " BRIGHT_DATA_BROWSER_AUTH=" " BRIGHT_DATA_API_TOKEN=" " BRIGHT_DATA_BROWSER_AUTH=" " Replace the placeholders with the values you got as explained in the Bright Data MCP server docs. Back in agent.py, read in those two envs like so: agent.py BRIGHT_DATA_API_TOKEN = os.getenv("BRIGHT_DATA_API_TOKEN") BRIGHT_DATA_BROWSER_AUTH = os.getenv("BRIGHT_DATA_BROWSER_AUTH") BRIGHT_DATA_API_TOKEN = os.getenv("BRIGHT_DATA_API_TOKEN") BRIGHT_DATA_BROWSER_AUTH = os.getenv("BRIGHT_DATA_BROWSER_AUTH") We’ll use these values shortly when initializing the MCP connection from code. 🧪 And just like that—bam 💥—your MCP server is up, running, and ready to serve your AI agent like a loyal sidekick. Onward! Step #3: Create Your AI Agent As teased in the intro of this section, we’re going to use Gemini (yep, Google’s shiny LLM) with the OpenAI Agents SDK. First off—can you even do that? And second—why not just use OpenAI directly? 🤔 can you even do that? why not just use OpenAI directly? Response: Yes, you can do that! 👍 Gemini exposes an OpenAI-compatible API, which means you can plug it right into the OpenAI Agents SDK like a drop-in replacement. The best part? It’s free—unlike OpenAI, which likes to charge you by the token. 💸 Response Gemini exposes an OpenAI-compatible API It’s free So, here’s why it makes sense to use Gemini instead of OpenAI directly! 💡 Enough chit-chat, let’s wire things up ⚡. First, grab your Gemini API key from Google AI Studio and pop it into your .env file: Gemini API key from Google AI Studio .env GEMINI_API_KEY=" " GEMINI_API_KEY=" " Then, in agent.py, read that key like so: agent.py GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") Now, build a function that ties everything together—the Gemini model, OpenAI SDK, and the MCP server (which we’ll configure in the code soon ⏱️): Gemini model, OpenAI SDK, and the MCP server async def create_mcp_ai_agent(mcp_server): # Initialize Gemini client using its OpenAI-compatible interface gemini_client = AsyncOpenAI( api_key=GEMINI_API_KEY, base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) # Set the default OpenAI client to Gemini set_default_openai_client(gemini_client) # Disable tracing to avoid tracing errors being logged in the terminal set_tracing_disabled(True) # Create an agent configured to use the MCP server and Gemini model agent = Agent( name="Assistant", instructions="You are a helpful assistant", model=OpenAIChatCompletionsModel( model="gemini-2.0-flash", openai_client=gemini_client, ), mcp_servers=[mcp_server] ) return agent async def create_mcp_ai_agent(mcp_server): # Initialize Gemini client using its OpenAI-compatible interface gemini_client = AsyncOpenAI( api_key=GEMINI_API_KEY, base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) # Set the default OpenAI client to Gemini set_default_openai_client(gemini_client) # Disable tracing to avoid tracing errors being logged in the terminal set_tracing_disabled(True) # Create an agent configured to use the MCP server and Gemini model agent = Agent( name="Assistant", instructions="You are a helpful assistant", model=OpenAIChatCompletionsModel( model="gemini-2.0-flash", openai_client=gemini_client, ), mcp_servers=[mcp_server] ) return agent If you want to use a different supported model (like Claude, Mistral, or other models), just swap the base_url and model name accordingly. base_url ➕ Extra: Want to use OpenAI instead? No problem. Just add your OPENAI_API_KEY and replace the above logic with: Extra OPENAI_API_KEY async def create_mcp_ai_agent(mcp_server): # Create an agent configured to use the MCP server and Gemini model agent = Agent( name="Assistant", instructions="You are a helpful assistant", mcp_servers=[mcp_server] ) return agent async def create_mcp_ai_agent(mcp_server): # Create an agent configured to use the MCP server and Gemini model agent = Agent( name="Assistant", instructions="You are a helpful assistant", mcp_servers=[mcp_server] ) return agent Easy swap, same interface—that’s the beauty of the SDK… 😎 Oh, and if you’re wondering where the mcp_server argument comes from—we’ll initialize it in the next step (👀 spoiler alert). Keep reading! mcp_server Step #4: Initialize the MCP Server Inside your run() async function, spin up the Bright Data MCP server using npx like this: run() npx async with MCPServerStdio( name="Bright Data web data MCP server, via npx", params={ "command": "npx", "args": ["-y", "@brightdata/mcp"], "env": { "API_TOKEN": BRIGHT_DATA_API_TOKEN, "BROWSER_AUTH": BRIGHT_DATA_BROWSER_AUTH, } }, client_session_timeout_seconds=180 # To avoid timeout errors ) as server: # Create and initialize the AI agent with the running MCP server agent = await create_mcp_ai_agent(server) # AI agent loop logic... async with MCPServerStdio( name="Bright Data web data MCP server, via npx", params={ "command": "npx", "args": ["-y", "@brightdata/mcp"], "env": { "API_TOKEN": BRIGHT_DATA_API_TOKEN, "BROWSER_AUTH": BRIGHT_DATA_BROWSER_AUTH, } }, client_session_timeout_seconds=180 # To avoid timeout errors ) as server: # Create and initialize the AI agent with the running MCP server agent = await create_mcp_ai_agent(server) # AI agent loop logic... ⚙️ What’s happening here? This launches the MCP server using npx -y @brightdata/mcp, passing in your credentials via environment variables. npx -y @brightdata/mcp Note: Don’t forget to bump client_session_timeout_seconds to 180 seconds (default is 5) — because real-time scraping, CAPTCHA solving, and anti-bot ninja moves performed by the Bright Data MCP server takes time 🥷. You don’t want it timing out mid-mission. client_session_timeout_seconds 180 5 real-time scraping, CAPTCHA solving, and anti-bot ninja moves ✅ Sweet! The only thing left is to kick off the AI agent loop... Let’s wrap it up. Step #5: Define the AI Agent Loop Inside the MCP server async block, drop in a REPL (Read-Eval-Print) loop so users can type in their requests, send them through the MCP-powered agent, and get back the magic ✨: while True: # Read the user's request request = input("Your request -> ") # Exit condition if request.lower() == "exit": print("Exiting the agent...") break # Run the request through the agent output = await Runner.run(agent, input=request) # Print the result to the user print(f"Output -> \n{output.final_output}\n\n") while True: # Read the user's request request = input("Your request -> ") # Exit condition if request.lower() == "exit": print("Exiting the agent...") break # Run the request through the agent output = await Runner.run(agent, input=request) # Print the result to the user print(f"Output -> \n{output.final_output}\n\n") This little Python snippet is your AI agent's command center. It continuously grabs your requests, feeds them to the agent, and then spits out the final, glorious result. Think of it as the no-frills console for your AI brain, ready to take orders until you tell it to “exit” 👋. Did you just have that “whoa” moment? If not, let me spell it out for you: you just wired up a full AI agent loop using the OpenAI Agents SDK + Bright Data’s MCP server + Gemini! Did you just have that “whoa” moment? you just wired up a full AI agent loop using the OpenAI Agents SDK + Bright Data’s MCP server + Gemini! Now you’re ready to look at the full code, all stitched together like a beautiful Frankenstein’s monster of AI automation. 🧌 Step #6: Put It All Together This is your final Python script for OpenAI Agents SDK + MCP integration (powered by Gemini to save money 🤑): import asyncio from dotenv import load_dotenv import os from agents import ( Runner, Agent, OpenAIChatCompletionsModel, set_default_openai_client, set_tracing_disabled ) from openai import AsyncOpenAI from agents.mcp import MCPServerStdio # Load environment variables from the .env file load_dotenv() # Read the required secrets envs from environment variables BRIGHT_DATA_API_TOKEN = os.getenv("BRIGHT_DATA_API_TOKEN") BRIGHT_DATA_BROWSER_AUTH = os.getenv("BRIGHT_DATA_BROWSER_AUTH") GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") async def create_mcp_ai_agent(mcp_server): # Initialize Gemini client using its OpenAI-compatible interface gemini_client = AsyncOpenAI( api_key=GEMINI_API_KEY, base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) # Set the default OpenAI client to Gemini set_default_openai_client(gemini_client) # Disable tracing to avoid tracing errors being logged in the terminal set_tracing_disabled(True) # Create an agent configured to use the MCP server and Gemini model agent = Agent( name="Assistant", instructions="You are a helpful assistant", model=OpenAIChatCompletionsModel( model="gemini-2.0-flash", openai_client=gemini_client, ), mcp_servers=[mcp_server] ) return agent async def run(): # Start the Bright Data MCP server via npx async with MCPServerStdio( name="Bright Data web data MCP server, via npx", params={ "command": "npx", "args": ["-y", "@brightdata/mcp"], "env": { "API_TOKEN": BRIGHT_DATA_API_TOKEN, "BROWSER_AUTH": BRIGHT_DATA_BROWSER_AUTH, } }, client_session_timeout_seconds=180 # To avoid timeout errors ) as server: # Create and initialize the AI agent with the running MCP server agent = await create_mcp_ai_agent(server) # Main REPL loop to process user requests while True: # Read the user's request request = input("Your request -> ") # Exit condition if request.lower() == "exit": print("Exiting the agent...") break # Run the request through the agent output = await Runner.run(agent, input=request) # Print the result to the user print(f"Output -> \n{output.final_output}\n\n") if __name__ == "__main__": asyncio.run(run()) import asyncio from dotenv import load_dotenv import os from agents import ( Runner, Agent, OpenAIChatCompletionsModel, set_default_openai_client, set_tracing_disabled ) from openai import AsyncOpenAI from agents.mcp import MCPServerStdio # Load environment variables from the .env file load_dotenv() # Read the required secrets envs from environment variables BRIGHT_DATA_API_TOKEN = os.getenv("BRIGHT_DATA_API_TOKEN") BRIGHT_DATA_BROWSER_AUTH = os.getenv("BRIGHT_DATA_BROWSER_AUTH") GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") async def create_mcp_ai_agent(mcp_server): # Initialize Gemini client using its OpenAI-compatible interface gemini_client = AsyncOpenAI( api_key=GEMINI_API_KEY, base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) # Set the default OpenAI client to Gemini set_default_openai_client(gemini_client) # Disable tracing to avoid tracing errors being logged in the terminal set_tracing_disabled(True) # Create an agent configured to use the MCP server and Gemini model agent = Agent( name="Assistant", instructions="You are a helpful assistant", model=OpenAIChatCompletionsModel( model="gemini-2.0-flash", openai_client=gemini_client, ), mcp_servers=[mcp_server] ) return agent async def run(): # Start the Bright Data MCP server via npx async with MCPServerStdio( name="Bright Data web data MCP server, via npx", params={ "command": "npx", "args": ["-y", "@brightdata/mcp"], "env": { "API_TOKEN": BRIGHT_DATA_API_TOKEN, "BROWSER_AUTH": BRIGHT_DATA_BROWSER_AUTH, } }, client_session_timeout_seconds=180 # To avoid timeout errors ) as server: # Create and initialize the AI agent with the running MCP server agent = await create_mcp_ai_agent(server) # Main REPL loop to process user requests while True: # Read the user's request request = input("Your request -> ") # Exit condition if request.lower() == "exit": print("Exiting the agent...") break # Run the request through the agent output = await Runner.run(agent, input=request) # Print the result to the user print(f"Output -> \n{output.final_output}\n\n") if __name__ == "__main__": asyncio.run(run()) 🎯 Just 81 lines of code. And what do you get? An AI agent that can surf the web, bypass anti-bots, grabs real-time data, and talks back—like it's no big deal. Even Superman might get jealous of this one. 🦸♂️ Thanks to new AI protocols and dev-first tools like OpenAI Agents SDK and Bright Data MCP, AI automation is now... almost too easy. AI protocols Step #7: Test the MCP-Powered AI Agent Run your AI agent from the terminal with: python agent.py python agent.py Or on Linux/macOS: python3 agent.py python3 agent.py You’ll be greeted by a friendly prompt like: Your request -> Your request -> For example, try asking the agent what MCP tools it has: What tools are you linked to through MCP? What tools are you linked to through MCP? You'll get back something like: As you can see, your AI agent is aware of all the Bright Data MCP tools it can connect to—and it’s ready to use them! Alright, time to unleash the beast! Let's put those shiny MCP tools to the ultimate test: Command your AI agent to scrape some juicy data from the Amazon P5 page 🎮: To achieve that, give it a prompt like: From the Amazon product page "https://www.amazon.com/PlayStation%C2%AE5-console-slim-PlayStation-5/dp/B0CL61F39H/", extract the main info and return it in JSON format From the Amazon product page "https://www.amazon.com/PlayStation%C2%AE5-console-slim-PlayStation-5/dp/B0CL61F39H/", extract the main info and return it in JSON format Execute it and the result will be: In particular, the output should be something like: { "title": "PlayStation®5 console (slim)", "seller_name": "Amazon.com", "brand": "Sony", "description": "The PS5 console unleashes new gaming possibilities that you never anticipated. Experience lightning fast loading with an ultra-high speed SSD, deeper immersion with support for haptic feedback, adaptive triggers, and 3D Audio*, and an all-new generation of incredible PlayStation games. Lightning Speed - Harness the power of a custom CPU, GPU, and SSD with Integrated I/O that rewrite the rules of what a PlayStation console can do. Stunning Games - Marvel at incredible graphics and experience new PS5 features. Play a back catalog of supported PS4 games. Breathtaking Immersion - Discover a deeper gaming experience with support for haptic feedback, adaptive triggers, and 3D Audio technology. Vertical stand sold separately. *3D audio via built-in TV speakers or analog/USB stereo headphones. Set up and latest system software update required.", "initial_price": 499, "currency": "USD", "availability": "In Stock", "reviews_count": 6759, "categories": [ "Video Games", "PlayStation 5", "Consoles" ], "asin": "B0CL61F39H", "buybox_seller": "Amazon.com", "number_of_sellers": 1, "root_bs_rank": 18, "answered_questions": 0, "domain": "https://www.amazon.com/", "images_count": 5, "url": "https://www.amazon.com/PlayStation%C2%AE5-console-slim-PlayStation-5/dp/B0CL61F39H?th=1&psc=1&language=en_US&currency=USD", "video_count": 6, "image_url": "https://m.media-amazon.com/images/I/41ECK5cY-2L._SL1000_.jpg", "item_weight": "10.6 pounds", "rating": 4.7, "product_dimensions": "14 x 17 x 7 inches; 10.6 Pounds", "seller_id": "ATVPDKIKX0DER", "date_first_available": "December 10, 2023", "model_number": "CFI-2015", "manufacturer": "Sony", "department": "Video Games", "plus_content": true, "video": true, "final_price": 499, "delivery": [ "FREE delivery Monday, May 26", "Or Prime members get FREE delivery Tomorrow, May 22. Order within 15 hrs 43 mins. Join Prime" ], "features": [ "Model Number CFI-2000", "Includes DualSense Wireless Controller, 1TB SSD, Disc Drive, 2 Horizontal Stand Feet, HDMI Cable, AC power cord, USB cable, printed materials, ASTRO’s PLAYROOM (Pre-installed game)", "Vertical Stand sold separately" ], "bought_past_month": 8000, "is_available": true, "root_bs_category": "Video Games", "bs_category": "PlayStation Consoles", "bs_rank": 15, "badge": "Amazon's Choice", "subcategory_rank": [ { "subcategory_name": "PlayStation 5 Consoles", "subcategory_rank": 1 } ], "amazon_choice": true, "product_details": [ { "type": "ASIN", "value": "B0CL61F39H" }, { "type": "Release date", "value": "December 10, 2023" }, { "type": "Customer Reviews", "value": "4.74.7 out of 5 stars6,759 ratings4.7 out of 5 stars" }, { "type": "Best Sellers Rank", "value": "#18 in Video Games (See Top 100 in Video Games)#1 in PlayStation 5 Consoles" }, { "type": "Product Dimensions", "value": "14 x 17 x 7 inches; 10.6 Pounds" }, { "type": "Type of item", "value": "Video Game" }, { "type": "Item model number", "value": "CFI-2015" }, { "type": "Item Weight", "value": "10.6 pounds" }, { "type": "Manufacturer", "value": "Sony" }, { "type": "Country of Origin", "value": "China" }, { "type": "Batteries", "value": "1 Lithium Ion batteries required. (included)" }, { "type": "Date First Available", "value": "December 10, 2023" } ], "country_of_origin": "China", "seller_url": "https://www.amazon.com/sp?ie=UTF8&seller=ATVPDKIKX0DER&asin=B0CL61F39H", "customers_say": "Customers find the PlayStation 5 console runs well and performs mind-blowingly fast, with quick loading times and smooth game performance. The graphics quality receives positive feedback, with one customer highlighting its stunning 120Hz visuals, while the console is easy to set up with an intuitive user interface. Customers consider the price worth the investment and appreciate its pristine condition. The noise level receives mixed reviews, with some customers finding it super quiet while others report it being loud.", "climate_pledge_friendly": false, "sponsered": true, "store_url": "https://www.amazon.com/stores/PlayStationPlayHasNoLimits/page/5AF5EF82-86EF-4699-B450-C232B3BD720E?lp_asin=B0CL61F39H&ref_=ast_bln&store_ref=bl_ast_dp_brandLogo_sto", "ships_from": "Amazon.com", "customers_say": { "text": "Customers find the PlayStation 5 console runs well and performs mind-blowingly fast, with quick loading times and smooth game performance. The graphics quality receives positive feedback, with one customer highlighting its stunning 120Hz visuals, while the console is easy to set up with an intuitive user interface. Customers consider the price worth the investment and appreciate its pristine condition. The noise level receives mixed reviews, with some customers finding it super quiet while others report it being loud.", "keywords": { "positive": [ "Functionality", "Gaming experience", "Value for money", "Graphics quality", "Quality", "Speed", "Ease of setup" ], "negative": null, "mixed": [ "Noise level" ] } }, "max_quantity_available": 30, "timestamp": "2025-05-21T10:46:13.487Z" } { "title": "PlayStation®5 console (slim)", "seller_name": "Amazon.com", "brand": "Sony", "description": "The PS5 console unleashes new gaming possibilities that you never anticipated. Experience lightning fast loading with an ultra-high speed SSD, deeper immersion with support for haptic feedback, adaptive triggers, and 3D Audio*, and an all-new generation of incredible PlayStation games. Lightning Speed - Harness the power of a custom CPU, GPU, and SSD with Integrated I/O that rewrite the rules of what a PlayStation console can do. Stunning Games - Marvel at incredible graphics and experience new PS5 features. Play a back catalog of supported PS4 games. Breathtaking Immersion - Discover a deeper gaming experience with support for haptic feedback, adaptive triggers, and 3D Audio technology. Vertical stand sold separately. *3D audio via built-in TV speakers or analog/USB stereo headphones. Set up and latest system software update required.", "initial_price": 499, "currency": "USD", "availability": "In Stock", "reviews_count": 6759, "categories": [ "Video Games", "PlayStation 5", "Consoles" ], "asin": "B0CL61F39H", "buybox_seller": "Amazon.com", "number_of_sellers": 1, "root_bs_rank": 18, "answered_questions": 0, "domain": "https://www.amazon.com/", "images_count": 5, "url": "https://www.amazon.com/PlayStation%C2%AE5-console-slim-PlayStation-5/dp/B0CL61F39H?th=1&psc=1&language=en_US&currency=USD", "video_count": 6, "image_url": "https://m.media-amazon.com/images/I/41ECK5cY-2L._SL1000_.jpg", "item_weight": "10.6 pounds", "rating": 4.7, "product_dimensions": "14 x 17 x 7 inches; 10.6 Pounds", "seller_id": "ATVPDKIKX0DER", "date_first_available": "December 10, 2023", "model_number": "CFI-2015", "manufacturer": "Sony", "department": "Video Games", "plus_content": true, "video": true, "final_price": 499, "delivery": [ "FREE delivery Monday, May 26", "Or Prime members get FREE delivery Tomorrow, May 22. Order within 15 hrs 43 mins. Join Prime" ], "features": [ "Model Number CFI-2000", "Includes DualSense Wireless Controller, 1TB SSD, Disc Drive, 2 Horizontal Stand Feet, HDMI Cable, AC power cord, USB cable, printed materials, ASTRO’s PLAYROOM (Pre-installed game)", "Vertical Stand sold separately" ], "bought_past_month": 8000, "is_available": true, "root_bs_category": "Video Games", "bs_category": "PlayStation Consoles", "bs_rank": 15, "badge": "Amazon's Choice", "subcategory_rank": [ { "subcategory_name": "PlayStation 5 Consoles", "subcategory_rank": 1 } ], "amazon_choice": true, "product_details": [ { "type": "ASIN", "value": "B0CL61F39H" }, { "type": "Release date", "value": "December 10, 2023" }, { "type": "Customer Reviews", "value": "4.74.7 out of 5 stars6,759 ratings4.7 out of 5 stars" }, { "type": "Best Sellers Rank", "value": "#18 in Video Games (See Top 100 in Video Games)#1 in PlayStation 5 Consoles" }, { "type": "Product Dimensions", "value": "14 x 17 x 7 inches; 10.6 Pounds" }, { "type": "Type of item", "value": "Video Game" }, { "type": "Item model number", "value": "CFI-2015" }, { "type": "Item Weight", "value": "10.6 pounds" }, { "type": "Manufacturer", "value": "Sony" }, { "type": "Country of Origin", "value": "China" }, { "type": "Batteries", "value": "1 Lithium Ion batteries required. (included)" }, { "type": "Date First Available", "value": "December 10, 2023" } ], "country_of_origin": "China", "seller_url": "https://www.amazon.com/sp?ie=UTF8&seller=ATVPDKIKX0DER&asin=B0CL61F39H", "customers_say": "Customers find the PlayStation 5 console runs well and performs mind-blowingly fast, with quick loading times and smooth game performance. The graphics quality receives positive feedback, with one customer highlighting its stunning 120Hz visuals, while the console is easy to set up with an intuitive user interface. Customers consider the price worth the investment and appreciate its pristine condition. The noise level receives mixed reviews, with some customers finding it super quiet while others report it being loud.", "climate_pledge_friendly": false, "sponsered": true, "store_url": "https://www.amazon.com/stores/PlayStationPlayHasNoLimits/page/5AF5EF82-86EF-4699-B450-C232B3BD720E?lp_asin=B0CL61F39H&ref_=ast_bln&store_ref=bl_ast_dp_brandLogo_sto", "ships_from": "Amazon.com", "customers_say": { "text": "Customers find the PlayStation 5 console runs well and performs mind-blowingly fast, with quick loading times and smooth game performance. The graphics quality receives positive feedback, with one customer highlighting its stunning 120Hz visuals, while the console is easy to set up with an intuitive user interface. Customers consider the price worth the investment and appreciate its pristine condition. The noise level receives mixed reviews, with some customers finding it super quiet while others report it being loud.", "keywords": { "positive": [ "Functionality", "Gaming experience", "Value for money", "Graphics quality", "Quality", "Speed", "Ease of setup" ], "negative": null, "mixed": [ "Noise level" ] } }, "max_quantity_available": 30, "timestamp": "2025-05-21T10:46:13.487Z" } 🤯 Wow! 🤯 Wow! 🤯 One prompt, and you just scraped all the data from an Amazon product page. And not just any site—Amazon is notoriously tough to scrape thanks to aggressive CAPTCHAs and anti-bot defenses. One prompt, and you just scraped all the data from an Amazon product page. So, how did that work? Behind the scenes, the AI agent called on the web_data_amazon_product MCP tool—one of the prebuilt power tools exposed by the Bright Data MCP server. It handled the dirty work: solving CAPTCHAs, bypassing bot detection, and fetching the goods. web_data_amazon_product And it doesn’t stop there! That scraped data? You can use it to ask the AI agent for further analysis by crafting more complex prompts. After all, AI agent can freely choose and chain together tools from its MCP-powered toolkit, so the possibilities are truly endless. ♾️ the possibilities are truly endless With a setup this powerful, the world is yours! (just don’t go full Tony Montana 😅) world is yours! Final Thoughts Now you know how to supercharge any AI agent built with the OpenAI Agents SDK—whether it's running on GPT, Gemini, or whatever’s next—by plugging it into an MCP server. In detail, we tapped into the powerful Bright Data MCP server, just one of the many services we've built to support real-world AI workflows at scale. Bright Data MCP server many services we've built to support real-world AI workflows at scale At Bright Data, our mission is simple: make AI accessible for everyone, everywhere. So until next time—stay curious, stay bold, and keep building the future of AI with creativity. 🌐