Apify MCP server

Connect your agent to the largest marketplace of tools for AI.

The official Apify Model Context Protocol (MCP) server gives AI agents web scraping, data extraction, and browser automation through thousands of ready-made Actors from Apify Store, at https://mcp.apify.com.

Apify is the marketplace of ready-to-run tools for AI agents. One connection to the Apify MCP server gives your agent thousands of tools to choose from, for jobs like web fetch, SERP monitoring, lead generation and enrichment, and pulling data from e-commerce sites and social media. The server loads only what each task needs.

Your agent searches Apify Store, checks an Actor's input and pricing, runs it, and reads the structured results, so you don't install a separate MCP server for every site. Connect to the hosted server with OAuth, or run it locally over stdio with the @apify/actors-mcp-server npm package.

Contents

• Quickstart
• Set up your client
• Remote (hosted) or local MCP server
• How an agent uses Apify Actors
• Tools, resources, and prompts
• Choose which tools load
• UI mode configuration
• Example prompts
• Pricing and limits
• Telemetry
• Troubleshooting and limitations
• FAQ
• Development
• Contributing
• Privacy and license
• Learn more

🚀 Quickstart

1. Open the MCP configurator, pick your client, and choose the tools you want.
2. Copy the generated configuration, deeplink, or command into your client.
3. Sign in to Apify in the browser window that opens. If you don't have an account, you can create a free one there.

The hosted endpoint is https://mcp.apify.com. If your client can't use OAuth, send your Apify API token in an Authorization: Bearer <APIFY_TOKEN> header instead. Treat the token like a password and keep it out of repositories.

Using a coding agent such as Claude Code, Codex, Cursor, or GitHub Copilot? An official Apify plugin installs this MCP server together with Apify agent skills in one step.

🤖 Set up your client

The configurator generates the exact setup for each supported client, including Claude Code, ChatGPT, Cursor, VS Code, GitHub Copilot CLI, Claude, Claude Desktop, Codex, OpenCode, Kiro, and more. The snippets below cover the most common cases.

claude mcp add apify "https://mcp.apify.com" -t http

apify mcp install cursor

{
  "servers": {
    "apify": {
      "type": "http",
      "url": "https://mcp.apify.com"
    }
  }
}

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com"
    }
  }
}

{
  "mcpServers": {
    "actors-mcp-server": {
      "command": "npx",
      "args": ["-y", "@apify/actors-mcp-server"],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_TOKEN"
      }
    }
  }
}

The server is also listed in the Docker MCP catalog, and each release ships an MCP bundle file (apify-mcp-server.mcpb) for one-click installation in clients that support it.

Remote (hosted) or local MCP server

The hosted remote MCP server with OAuth is the recommended option. Use the local server when you want to run the MCP process on your own machine; Actors still run and store their results on the Apify platform in both modes. If your client supports only stdio, you can also bridge it to the hosted server with mcp-remote: npx mcp-remote https://mcp.apify.com.

How an agent uses Apify Actors

Actors are serverless programs on the Apify platform. Each one does a specific job, like scraping Google Maps, crawling a website for RAG, or collecting social media posts. The MCP server turns Actors into tools an agent can find and call without you configuring each one in advance.

A typical task follows four steps:

1. search-actors finds candidate Actors in Apify Store for the task.
2. fetch-actor-details returns the Actor's input schema, README, and pricing, plus its output schema on request.
3. call-actor starts the run and waits for it to finish, 30 seconds by default and up to 45. It returns the run status and storage IDs; the scraped items stay in the run's dataset.
4. get-dataset-items reads the results from the run's dataset, with limit and offset for pagination.

The server adds get-dataset-items, `get-key-