mcp.apify.com

The Apify Model Context Protocol (MCP) server at mcp.apify.com enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from the Apify Store. It supports OAuth, allowing you to connect from clients like Claude.ai or Visual Studio Code using just the URL.

🚀 Use the hosted Apify MCP Server!

For the best experience, connect your AI assistant to our hosted server at https://mcp.apify.com. The hosted server supports the latest features - including output schema inference for structured Actor results - that are not available when running locally via stdio.

💰 The server also supports Skyfire agentic payments, allowing AI agents to pay for Actor runs without an API token.

Apify MCP Server is compatible with Claude Code, Claude.ai, Cursor, VS Code and any client that adheres to the Model Context Protocol. Check out the MCP clients section for more details or visit the MCP configuration page.

Table of Contents

• 🌐 Introducing the Apify MCP server
• 🚀 Quickstart
• ⚠️ SSE transport deprecation
• 🤖 MCP clients
• 🪄 Try Apify MCP instantly
• 💰 Skyfire agentic payments
• 🛠️ Tools, resources, and prompts
• 📊 Telemetry
• 🐛 Troubleshooting (local MCP server)
• ⚙️ Development
• 🤝 Contributing
• 📚 Learn more

🌐 Introducing the Apify MCP server

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:

• Use Facebook Posts Scraper to extract data from Facebook posts from multiple pages/profiles.
• Use Google Maps Email Extractor to extract contact details from Google Maps.
• Use Google Search Results Scraper to scrape Google Search Engine Results Pages (SERPs).
• Use Instagram Scraper to scrape Instagram posts, profiles, places, photos, and comments.
• Use RAG Web Browser to search the web, scrape the top N URLs, and return their content.

Video tutorial: Integrate 8,000+ Apify Actors and Agents with Claude

🚀 Quickstart

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 streamable transport

Standard Input/Output (stdio): Ideal for local integrations and command-line tools like the Claude for Desktop client.

• Set the MCP client server command to npx @apify/actors-mcp-server and the APIFY_TOKEN environment variable to your Apify API token.
• See npx @apify/actors-mcp-server --help for more options.

You can find detailed instructions for setting up the MCP server in the Apify documentation.

⚠️ SSE transport deprecation on April 1, 2026

Update your MCP client config before April 1, 2026. The Apify MCP server is dropping Server-Sent Events (SSE) transport in favor of Streamable HTTP, in line with the official MCP spec.

Go to mcp.apify.com to update the installation for your client of choice, with a valid endpoint.

🤖 MCP clients

Apify MCP Server is compatible with any MCP client that adheres to the Model Context Protocol, but the level of support for dynamic tool discovery and other features may vary between clients.

To interact with the Apify MCP server, you can use clients such as: Claude Desktop, Visual Studio Code, or Apify Tester MCP Client.

Visit mcp.apify.com to configure the server for your preferred client.

Supported clients matrix

The following table outlines the tested MCP clients and their level of support for key features.

Client	Dynamic Tool Discovery	Notes
Claude.ai (web)	🟡 Partial	Tools mey need to be reloaded manually in the client
Claude Desktop	🟡 Partial	Tools may need to be reloaded manually in the client
VS Code (Genie)	✅ Full
Cursor	✅ Full
Apify Tester MCP Client	✅ Full	Designed for testing Apify MCP servers
OpenCode	✅ Full

Smart tool selection based on client capabilities:

When the actors tool category is requested, the server intelligently selects the most appropriate Actor-related tools based on the client's capabilities:

• Clients with dynamic tool support (e.g., Claude.ai web, VS Code Genie): The server provides the add-actor tool instead of call-actor. This allows for a better user experience where users can dynamically discover and add new Actors as tools during their conversation.

• Clients with limited dynamic tool support (e.g., Claude Desktop): The server provides the standard call-actor tool along with other Actor category tools, ensuring compatibility while maintaining functionality.

🪄 Try Apify MCP instantly

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 MCP bundle file (formerly known as Anthropic Desktop extension file, or DXT) for one-click installation: Apify MCP server MCPB file

💰 Skyfire agentic payments

The Apify MCP Server integrates with Skyfire to enable agentic payments - AI agents can autonomously pay for Actor runs without requiring an Apify API token. Instead of authenticating with APIFY_TOKEN, the agent uses Skyfire PAY tokens to cover billing for each tool call.

Prerequisites:

• A Skyfire account with a funded wallet
• An MCP client that supports multiple servers (e.g., Claude Desktop, OpenCode, VS Code)

Setup:

Configure both the Skyfire MCP server and the Apify MCP server in your MCP client. Enable payment mode by adding the payment=skyfire query parameter to the Apify server URL:

{
  "mcpServers": {
    "skyfire": {
      "url": "https://api.skyfire.xyz/mcp/sse",
      "headers": {
        "skyfire-api-key": "<YOUR_SKYFIRE_API_KEY>"
      }
    },
    "apify": {
      "url": "https://mcp.apify.com?payment=skyfire"
    }
  }
}

How it works:

When Skyfire mode is enabled, the agent handles the full payment flow autonomously:

1. The agent discovers relevant Actors via search-actors or fetch-actor-details (these remain free).
2. Before executing an Actor, the agent creates a PAY token using the create-pay-token tool from the Skyfire MCP server (minimum $5.00 USD).
3. The agent passes the PAY token in the skyfire-pay-id input property when calling the Actor tool.
4. Results are returned as usual. Unused funds on the token remain available for future runs or are returned upon expiration.

To learn more, see the Skyfire integration documentation and the Agentic Payments with Skyfire blog post.

🛠️ Tools, resources, and prompts

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.

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:

{
  "query": "restaurants in San Francisco",
  "maxResults": 3
}

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.

Helper tools

One of the most powerful features of using MCP with Apify is dynamic tool discovery. It allows an AI agent to find new tools (Actors) as needed and incorporate them. Here are some special MCP operations and how the Apify MCP Server supports them:

• Apify Actors: Search for Actors, view their details, and use them as tools for the AI.
• Apify documentation: Search the Apify documentation and fetch specific documents to provide context to the AI.
• Actor runs: Get lists of your Actor runs, inspect their details, and retrieve logs.
• Apify storage: Access data from your datasets and key-value stores.

Overview of available tools

Here is an overview list of all the tools provided by the Apify MCP Server.

Tool name	Category	Description	Enabled by default
search-actors	actors	Search for Actors in the Apify Store.	✅
fetch-actor-details	actors	Retrieve detailed information about a specific Actor, including its input schema, README (summary when available, full otherwise), pricing, and Actor output schema.	✅
call-actor*	actors	Call an Actor and get its run results. Use fetch-actor-details first to get the Actor's input schema.	❔
get-actor-run	runs	Get detailed information about a specific Actor run.
get-actor-output*	-	Retrieve the output from an Actor call which is not included in the output preview of the Actor tool.	✅
search-apify-docs	docs	Search the Apify documentation for relevant pages.	✅
fetch-apify-docs	docs	Fetch the full content of an Apify documentation page by its URL.	✅
apify-slash-rag-web-browser	Actor (see tool configuration)	An Actor tool to browse the web.	✅
get-actor-run-list	runs	Get a list of an Actor's runs, filterable by status.
get-actor-log	runs	Retrieve the logs for a specific Actor run.
get-dataset	storage	Get metadata about a specific dataset.
get-dataset-items	storage	Retrieve items from a dataset with support for filtering and pagination.
get-dataset-schema	storage	Generate a JSON schema from dataset items.
get-key-value-store	storage	Get metadata about a specific key-value store.
get-key-value-store-keys	storage	List the keys within a specific key-value store.
get-key-value-store-record	storage	Get the value associated with a specific key in a key-value store.
get-dataset-list	storage	List all available datasets for the user.
get-key-value-store-list	storage	List all available key-value stores for the user.
add-actor*	experimental	Add an Actor as a new tool for the user to call.	❔

Note:

When using the actors tool category, clients that support dynamic tool discovery (like Claude.ai web and VS Code) automatically receive the add-actor tool instead of call-actor for enhanced Actor discovery capabilities.

The get-actor-output tool is automatically included with any Actor-related tool, such as call-actor, add-actor, or any specific Actor tool like apify-slash-rag-web-browser. When you call an Actor - either through the call-actor tool or directly via an Actor tool (e.g., apify-slash-rag-web-browser) - you receive a preview of the output. The preview depends on the Actor's output format and length; for some Actors and runs, it may include the entire output, while for others, only a limited version is returned to avoid overwhelming the LLM. To retrieve the full output of an Actor run, use the get-actor-output tool (supports limit, offset, and field filtering) with the datasetId provided by the Actor call.

Tool annotations

All tools include metadata annotations to help MCP clients and LLMs understand tool behavior:

• title: Short display name for the tool (e.g., "Search Actors", "Call Actor", "apify/rag-web-browser")
• readOnlyHint: true for tools that only read data without modifying state (e.g., get-dataset, fetch-actor-details)
• openWorldHint: true for tools that access external resources outside the Apify platform (e.g., call-actor executes external Actors, get-html-skeleton scrapes external websites). Tools that interact only with the Apify platform (like search-actors or fetch-apify-docs) do not have this hint.

Tools configuration

The tools configuration parameter is used to specify loaded tools - either categories or specific tools directly, and Apify Actors. For example, tools=storage,runs loads two categories; tools=add-actor loads just one tool.

When no query parameters are provided, the MCP server loads the following tools by default:

• actors
• docs
• apify/rag-web-browser

If the tools parameter is specified, only the listed tools or categories will be enabled - no default tools will be included.

Easy configuration:

Use the UI configurator to configure your server, then copy the configuration to your client.

Configuring the hosted server:

The hosted server can be configured using query parameters in the URL. For example, to load the default tools, use:

https://mcp.apify.com?tools=actors,docs,apify/rag-web-browser

For minimal configuration, if you want to use only a single Actor tool - without any discovery or generic calling tools, the server can be configured as follows:

https://mcp.apify.com?tools=apify/my-actor

This setup exposes only the specified Actor (apify/my-actor) as a tool. No other tools will be available.

Configuring the CLI:

The CLI can be configured using command-line flags. For example, to load the same tools as in the hosted server configuration, use:

npx @apify/actors-mcp-server --tools actors,docs,apify/rag-web-browser

The minimal configuration is similar to the hosted server configuration:

npx @apify/actors-mcp-server --tools apify/my-actor

As above, this exposes only the specified Actor (apify/my-actor) as a tool. No other tools will be available.

⚠️ Important recommendation

The default tools configuration may change in future versions. When no tools parameter is specified, the server currently loads default tools, but this behavior is subject to change.

For production use and stable interfaces, always explicitly specify the tools parameter to ensure your configuration remains consistent across updates.

UI mode configuration

The uiMode parameter enables OpenAI-specific widget rendering in tool responses. When enabled, tools like search-actors return interactive widget responses optimized for OpenAI clients.

Configuring the hosted server:

Enable UI mode using the ui query parameter:

https://mcp.apify.com?ui=openai

You can combine it with other parameters:

https://mcp.apify.com?tools=actors,docs&ui=openai

Configuring the CLI:

The CLI can be configured using command-line flags. For example, to enable UI mode:

npx @apify/actors-mcp-server --ui openai

You can also set it via the UI_MODE environment variable:

export UI_MODE=openai
npx @apify/actors-mcp-server

Backward compatibility

The v2 configuration preserves backward compatibility with v1 usage. Notes:

• actors param (URL) and --actors flag (CLI) are still supported.
  • Internally they are merged into tools selectors.
  • Examples: ?actors=apify/rag-web-browser ≡ ?tools=apify/rag-web-browser; --actors apify/rag-web-browser ≡ --tools apify/rag-web-browser.
• enable-adding-actors (CLI) and enableAddingActors (URL) are supported but deprecated.
  • Prefer tools=experimental or including the specific tool tools=add-actor.
  • Behavior remains: when enabled with no tools specified, the server exposes only add-actor; when categories/tools are selected, add-actor is also included.
• enableActorAutoLoading remains as a legacy alias for enableAddingActors and is mapped automatically.
• Defaults remain compatible: when no tools are specified, the server loads actors, docs, and apify/rag-web-browser.
  • If any tools are specified, the defaults are not added (same as v1 intent for explicit selection).
• call-actor is now included by default via the actors category (additive change). To exclude it, specify an explicit tools list without actors.
• preview category is deprecated and removed. Use specific tool names instead.

Existing URLs and commands using ?actors=... or --actors continue to work unchanged.

Prompts

The server provides a set of predefined example prompts to help you get started interacting with Apify through MCP. For example, there is a GetLatestNewsOnTopic prompt that allows you to easily retrieve the latest news on a specific topic using the RAG Web Browser Actor.

Resources

The server does not yet provide any resources.

📡 Telemetry

The Apify MCP Server collects telemetry data about tool calls to help Apify understand usage patterns and improve the service. By default, telemetry is enabled for all tool calls.

The stdio transport also uses Sentry for error tracking, which helps us identify and fix issues faster. Sentry is automatically disabled when telemetry is opted out.

Opting out of telemetry

You can opt out of telemetry (including Sentry error tracking) by setting the --telemetry-enabled CLI flag to false or the TELEMETRY_ENABLED environment variable to false. CLI flags take precedence over environment variables.

Examples

For the remote server (mcp.apify.com):

# Disable via URL parameter
https://mcp.apify.com?telemetry-enabled=false

For the local stdio server:

# Disable via CLI flag
npx @apify/actors-mcp-server --telemetry-enabled=false

# Or set environment variable
export TELEMETRY_ENABLED=false
npx @apify/actors-mcp-server

⚙️ Development

Please see the CONTRIBUTING.md guide for contribution guidelines and commit message conventions.

For detailed development setup, project structure, and local testing instructions, see the DEVELOPMENT.md guide.

Prerequisites

• Node.js (v18 or higher)

Create an environment file, .env, with the following content:

APIFY_TOKEN="your-apify-token"

Build the actor-mcp-server package:

npm run build

Start HTTP streamable MCP server

Run using Apify CLI:

export APIFY_TOKEN="your-apify-token"
export APIFY_META_ORIGIN=STANDBY
apify run -p

Once the server is running, you can use the MCP Inspector to debug the server exposed at http://localhost:3001.

Start standard input/output (stdio) MCP server

You can launch the MCP Inspector with this command:

export APIFY_TOKEN="your-apify-token"
npx @modelcontextprotocol/inspector node ./dist/stdio.js

Upon launching, the Inspector will display a URL that you can open in your browser to begin debugging.

Unauthenticated access

When the tools query parameter includes only tools explicitly enabled for unauthenticated use, the hosted server allows access without an API token. Currently allowed tools: search-actors, fetch-actor-details, search-apify-docs, fetch-apify-docs. Example: https://mcp.apify.com?tools=search-actors.

🐦 Canary PR releases

Apify MCP is split across two repositories: this one for core MCP logic and the private apify-mcp-server-internal for the hosted server. Changes must be synchronized between both.

To create a canary release, add the beta tag to your PR branch. This publishes the package to pkg.pr.new for staging and testing before merging. See the workflow file for details.

🐋 Docker Hub integration

The Apify MCP Server is also available on Docker Hub, registered via the mcp-registry repository. The entry in servers/apify-mcp-server/server.yaml should be deployed automatically by the Docker Hub MCP registry (deployment frequency is unknown). Before making major changes to the stdio server version, be sure to test it locally to ensure the Docker build passes. To test, change the source.branch to your PR branch and run task build -- apify-mcp-server. For more details, see CONTRIBUTING.md.

🐛 Troubleshooting (local MCP server)

• Make sure you have node installed by running node -v.
• Make sure the APIFY_TOKEN environment variable is set.
• Always use the latest version of the MCP server by using @apify/actors-mcp-server@latest.

Debugging the NPM package

To debug the server, use the MCP Inspector tool:

export APIFY_TOKEN="your-apify-token"
npx @modelcontextprotocol/inspector npx -y @apify/actors-mcp-server

💡 Limitations

The Actor input schema is processed to be compatible with most MCP clients while adhering to JSON Schema standards. The processing includes:

• Descriptions are truncated to 500 characters (as defined in MAX_DESCRIPTION_LENGTH).
• Enum fields are truncated to a maximum combined length of 2000 characters for all elements (as defined in ACTOR_ENUM_MAX_LENGTH).
• Required fields are explicitly marked with a REQUIRED prefix in their descriptions for compatibility with frameworks that may not handle the JSON schema properly.
• Nested properties are built for special cases like proxy configuration and request list sources to ensure the correct input structure.
• Array item types are inferred when not explicitly defined in the schema, using a priority order: explicit type in items > prefill type > default value type > editor type.
• Enum values and examples are added to property descriptions to ensure visibility, even if the client doesn't fully support the JSON schema.
• Rental Actors are only available for use with the hosted MCP server at https://mcp.apify.com. When running the server locally via stdio, you can only access Actors that are already added to your local toolset. To dynamically search for and use any Actor from the Apify Store—including rental Actors—connect to the hosted endpoint.

🤝 Contributing

We welcome contributions to improve the Apify MCP Server! Here's how you can help:

• 🐛 Report issues: Find a bug or have a feature request? Open an issue.
• 🔧 Submit pull requests: Fork the repo and submit pull requests with enhancements or fixes.
• 📚 Documentation: Improvements to docs and examples are always welcome.
• 💡 Share use cases: Contribute examples to help other users.

For major changes, please open an issue first to discuss your proposal and ensure it aligns with the project's goals.

📚 Learn more

• Model Context Protocol
• What are AI Agents?
• What is MCP and why does it matter?
• How to use MCP with Apify Actors
• Tester MCP Client
• Webinar: Building and Monetizing MCP Servers on Apify
• How to build and monetize an AI agent on Apify