Oxylabs AI Studio MCP Server

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📖 Overview

The Oxylabs AI Studio MCP server provides various AI tools for your agents.:

• Scrape: Allows getting content from any url in json or markdown format.
• Crawl: Based on prompt crawls a website and collects data in markdown or json format.
• Browser Agent: Given a task, agent controls a browser to achieve given object and returns data in markdown, json, html or screenshot formats.
• Search: Allows search the web for urls and their contents.

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✅ Prerequisites

Obtain your Api Key from Oxylabs AI Studio dashboard:

Basic Usage

Python >=3.10+ To run the server best to use uv:

• install it using this guide
• To setup with cursor the easiest way is to use uvx.

cursor uvx

{
  "mcpServers": {
    "oxylabs-ai-studio": {
      "command": "uvx",
      "args": ["oxylabs-ai-studio-mcp"],
      "env": {
        "OXYLABS_AI_STUDIO_API_KEY": "OXYLABS_AI_STUDIO_API_KEY"
      }
    }
  }
}

You can also pull the project and run it locally with uv in cursor:

{
  "mcpServers": {
    "oxylabs-ai-studio": {
      "command": "uv",
      "args": [
        "--directory",
        "/<absolute-path-to-folder>/oxylabs-ai-studio-mcp",
        "run",
        "oxylabs-ai-studio-mcp"
      ],
      "env": {
        "OXYLABS_AI_STUDIO_API_KEY": "OXYLABS_AI_STUDIO_API_KEY"
      }
    }
  }
}

Agent example:

import os
import asyncio

from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStdio

server = MCPServerStdio(  
    'uvx',
    args=[
          'oxylabs-ai-studio-mcp',
    ],
    env={
        "OXYLABS_AI_STUDIO_API_KEY": "<your_api_key>",
    },
)
# requires OPENAI_API_KEY to be set.
agent = Agent('openai:gpt-4o', mcp_servers=[server])


async def main():
   
    async with agent.run_mcp_servers():
        result = await agent.run('Got to oxylabs careers page and first first available job and return the job title and description.')
        print(result.output)

if __name__ == "__main__":
    asyncio.run(main())