Oxylabs MCP Server

What does “Oxylabs” MCP Server do?

The Oxylabs MCP (Model Context Protocol) Server is a bridge between AI assistants and the real-world web, providing a unified API to deliver clean, structured data from any site. By integrating with the MCP ecosystem, this server allows AI models and agents to access, query, and utilize external data sources on demand. This enables tasks such as automated web data extraction, enrichment of AI workflows with live information, and streamlined access to web content for large language models. The Oxylabs MCP Server is designed to enhance development workflows by allowing seamless interactions between AI clients and the web, making it valuable for developers who need programmatic access to comprehensive, real-time data.

List of Prompts

No prompt templates are mentioned in the accessible repository content.

List of Resources

No explicit MCP resources are mentioned in the accessible repository content.

List of Tools

No server.py or tool definitions are visible in the accessible repository content.

Use Cases of this MCP Server

• Web Data Extraction: Enables developers to fetch structured data from nearly any website, automating data collection for research, analytics, or monitoring.
• AI Workflow Enrichment: Allows AI assistants to supplement their responses with real-time web data, increasing accuracy and relevance in tasks such as customer support or content generation.
• Market Intelligence: Facilitates the collection of competitor pricing, product listings, and industry trends for business and data analysts.
• Content Aggregation: Powers aggregation platforms by sourcing, normalizing, and serving content from multiple online channels for news, blogs, or forums.
• Research Automation: Assists researchers in programmatically gathering large datasets from the web, supporting data-driven insights and studies.

How to set it up

Windsurf

1. Ensure prerequisites are met (Node.js, etc.).
2. Locate your configuration file (e.g., windsurf.config.json).
3. Add the Oxylabs MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "oxylabs-mcp": {
         "command": "npx",
         "args": ["@oxylabs/oxylabs-mcp@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking the MCP server status in Windsurf.

Claude

1. Confirm Claude platform prerequisites.
2. Open the relevant Claude configuration file.
3. Add the Oxylabs MCP Server configuration:

   {
     "mcpServers": {
       "oxylabs-mcp": {
         "command": "npx",
         "args": ["@oxylabs/oxylabs-mcp@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm the server is active and accessible.

Cursor

1. Install required dependencies (Node.js, etc.).
2. Open Cursor’s configuration file.
3. Insert the following MCP server definition:

   {
     "mcpServers": {
       "oxylabs-mcp": {
         "command": "npx",
         "args": ["@oxylabs/oxylabs-mcp@latest"]
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Check the MCP server connection in the Cursor UI.

Cline

1. Make sure system prerequisites are installed.
2. Edit the Cline configuration file.
3. Add the MCP server entry:

   {
     "mcpServers": {
       "oxylabs-mcp": {
         "command": "npx",
         "args": ["@oxylabs/oxylabs-mcp@latest"]
       }
     }
   }
   

4. Save the file and restart Cline.
5. Verify Oxylabs MCP Server is running in Cline.

Securing API keys:
Store sensitive API keys using environment variables. Example:

{
  "mcpServers": {
    "oxylabs-mcp": {
      "command": "npx",
      "args": ["@oxylabs/oxylabs-mcp@latest"],
      "env": {
        "OXYLABS_API_KEY": "${OXYLABS_API_KEY}"
      },
      "inputs": {
        "apiKey": "${OXYLABS_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "oxylabs-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “oxylabs-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Between the overview and details available, the Oxylabs MCP Server provides well-documented setup instructions and a clear overview, but lacks visible details on prompts, resources, and tools in the provided files.

Our opinion

The Oxylabs MCP Server is professionally presented and easy to set up, with a credible license and good documentation. However, the lack of visible prompt templates, resource definitions, and tool descriptions makes it less informative for developers seeking to understand its full capabilities out-of-the-box. Based on the above, I would rate this MCP server a 4/10 for completeness and developer readiness, mainly due to missing technical details.

MCP Score