The official Apify MCP server connects an AI agent to thousands of ready-built Apify Actors for web scraping, data extraction, and automation, either via a hosted OAuth endpoint or a local npm package.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
We ran the install command below but it didn't respond within our test window β this can mean a slow first-time install rather than a real problem.
npx -y @apify/actors-mcp-serverNo response to initialize.
This is an experimental automated check and can have false negatives β missing environment variables, a slow cold install, etc. It doesnβt necessarily mean somethingβs wrong. Last checked 7d ago.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Jirispilka Actors MCP Server.
The Apify Model Context Protocol (MCP) Server at mcp.apify.com instantly connects AI applications and agents to thousands of readyβbuilt tools. It allows your AI assistant to use any Apify Actor for web scraping, data extraction, and automation tasks in real time.
π Try the hosted Apify MCP Server!
For the easiest setup and most powerful features, including the ability to find and use any Actor from Apify Store, connect your AI assistant to our hosted server:
It supports OAuth, so you can connect from clients like Claude.ai or Visual Studio Code with just the URL.

The Apify MCP Server allows an AI assistant to use any Apify Actor as a tool to perform a specific task. For example, it can:
Video tutorial: Integrate 5,000+ Apify Actors and Agents with Claude
You can use the Apify MCP Server in two ways:
HTTPS Endpoint (mcp.apify.com): Connect from your MCP client via OAuth or by including the Authorization: Bearer <APIFY_TOKEN> header in your requests. This is the recommended method for most use cases. Because it supports OAuth, you can connect from clients like Claude.ai or Visual Studio Code using just the URL: https://mcp.apify.com.
https://mcp.apify.com (recommended) for streamable transporthttps://mcp.apify.com/sse for legacy SSE transportStandard Input/Output (stdio): Ideal for local integrations and command-line tools like the Claude for Desktop client.
npx @apify/actors-mcp-server and the APIFY_TOKEN environment variable to your Apify API token.npx @apify/actors-mcp-server --help for more options.You can find detailed instructions for setting up the MCP server in the Apify documentation.
To interact with the Apify MCP server, you can use various MCP clients, such as:
With MCP server integrated, you can ask your AI assistant things like:
The following table outlines the tested MCP clients and their level of support for key features.
| Client | Dynamic Tool Discovery | Notes |
|---|---|---|
| Claude.ai (web) | β Full | |
| Claude Desktop | π‘ Partial | Tools may need to be reloaded manually in the client. |
| VS Code (Genie) | β Full | |
| LibreChat | β Untested | |
| Apify Tester MCP Client | β Full | Designed for testing Apify MCP servers. |
This matrix is a work in progress. If you have tested other clients, please consider contributing to this documentation.
Want to try Apify MCP without any setup?
Check out Apify Tester MCP Client
This interactive, chat-like interface provides an easy way to explore the capabilities of Apify MCP without any local setup. Just sign in with your Apify account and start experimenting with web scraping, data extraction, and automation tools!
Or use the Anthropic Desktop extension file (dxt) for one-click installation: Apify MCP server dxt file
The MCP server provides a set of tools for interacting with Apify Actors. Since the Apify Store is large and growing rapidly, the MCP server provides a way to dynamically discover and use new Actors.
Any Apify Actor can be used as a tool.
By default, the server is pre-configured with one Actor, apify/rag-web-browser, and several helper tools.
The MCP server loads an Actor's input schema and creates a corresponding MCP tool.
This allows the AI agent to know exactly what arguments to pass to the Actor and what to expect in return.
For example, for the apify/rag-web-browser Actor, the input parameters are:
You don't need to manually specify which Actor to call or its input parameters; the LLM handles this automatically. When a tool is called, the arguments are automatically passed to the Actor by the LLM. You can refer to the specific Actor's documentation for a list of available arguments.
One of the most powerful features of using MCP with Apify is dynamic tool discovery. It gives an AI agent the ability to find new tools (Actors) as needed and incorporate them. Here are some special MCP operations and how the Apify MCP Server supports them:
Note: Helper tool categories marked with (*) are not enabled by default in the MCP server and must be explicitly enabled using the tools argument (either the --tools command line argument for the stdio server or the ?tools URL query parameter for the remote MCP server). The tools argument is a comma-separated list of categories with the following possible values:
docs: Search and fetch Apify documentation tools.runs: Get Actor run lists, run details, and logs from a specific Actor run.storage: Access datasets, key-value stores, and their records.preview: Experimental tools in preview mode.For example, to enable all tools, use npx @apify/actors-mcp-server --tools docs,runs,storage,preview or https://mcp.apify.com/?tools=docs,runs,storage,preview.
Here is an overview list of all the tools provided by the Apify MCP Server.
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