Apify MCP Server

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](https://apify.com/store), at [`https://mcp.apify.com`](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 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 Code

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

Claude Code opens the Apify sign-in page the first time it uses the server. To install the Apify plugin with Apify agent skills instead, run /plugin marketplace add apify/apify-claude-code-plugin, then /plugin install apify@apify and /reload-plugins.

ChatGPT

1. Open ChatGPT Plugins, select +, then Add custom MCP server.
2. Name it Apify, enter https://mcp.apify.com as the server URL, and choose OAuth authentication.
3. Review the risk warning, select I understand and want to continue, then Create as a plugin.
4. Sign in with Apify, review the tools, and install the plugin. No API token is needed.
5. Start a new conversation, type @, and select Apify.

In Business, Enterprise, and Edu workspaces, an admin or owner may need to create or publish the plugin. If the option is missing, ask your workspace admin. When the server's tools change, open the plugin and select Refresh.

The ChatGPT desktop app uses local MCP configuration shared with Codex instead: go to Settings > MCP servers > Add server, choose Streamable HTTP, and enter https://mcp.apify.com. See OpenAI's custom MCP server guide.

Cursor

Use the one-click install link from the configurator, or let the Apify CLI write the configuration, including your API token:

apify mcp install cursor

The same command works for claude-code, vscode, codex, kiro, and antigravity.

VS Code and GitHub Copilot

Use the one-click install link from the configurator, run apify mcp install vscode, or add https://mcp.apify.com as an HTTP server in your MCP configuration:

{

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

Claude

Apify is available as a Claude connector. Open Settings > Connectors, find Apify in the connector directory, and connect it. You can also select Add custom connector, name it Apify, and enter https://mcp.apify.com as the remote MCP server URL. Sign in with Apify when prompted. The same connector works on Claude.ai and in Claude Desktop. See the Claude guide.

Any other MCP client (JSON config)

Most clients that support remote servers and OAuth accept a configuration like this:

{

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

Local stdio (npx)

Requires Node.js 22 or later.

{

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

If you've run apify login, the server can also read the token saved by the Apify CLI. Run npx @apify/actors-mcp-server --help for all options.

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-value-store-record, get-actor-run, and abort-actor-run whenever call-actor or an Actor tool is loaded, so the agent can always read results and check on long runs that outlast the wait.

You can also expose a specific Actor as its own tool, for example apify/instagram-scraper. The server builds the tool's parameters from the Actor's input schema, so the agent knows exactly what to pass. A Server Actor with an API schema, such as apify/web-fetch, apify/web-search, or apify/rag-web-browser, gets a tool that sends one HTTP request to its API instead.

Actors that run their own MCP server. Some Actors are MCP servers themselves. fetch-actor-mcp-tool-list lists their tools, and call-actor-mcp-tool calls one. When such an Actor is loaded as a tool source and has no API schema, its tools are exposed under names in the form username--actor-name--tool-name; an Actor with an API schema, such as apify/web-search, gets its server tool instead, and call-actor-mcp-tool reaches its MCP tools. Apify Store also lists MCP-related Actors from independent creators; those are separate products with their own READMEs and pricing.

🛠️ Tools, resources, and prompts

Any Actor can be a tool, and agents discover new ones as they work. By default, the server lo