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\n \n \n \n $\"Apify$ \n \n \n
\n mcp.apify.com\n

\n\n

\n $\"NPM$ \n $\"Downloads\"$ \n $\"Build$ \n $\"smithery$ \n

\n\n\nThe Apify Model Context Protocol (MCP) server at [**mcp.apify.com**](https://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 [Apify Store](https://apify.com/store). It supports OAuth, allowing you to connect from clients like Claude.ai or Visual Studio Code using just the URL.\n\n> **🚀 Use the hosted Apify MCP Server!**\n>\n> For the best experience, connect your AI assistant to our hosted server at **[`https://mcp.apify.com`](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.\n\n> ⚠️ **Legacy SSE transport removed.** The `https://mcp.apify.com/sse` endpoint has been removed in favor of Streamable HTTP. Migrate your client to **[`https://mcp.apify.com`](https://mcp.apify.com)** — drop the `/sse` suffix from your configuration.\n\n💰 The server also supports [agentic payments](#-agentic-payments) via [x402](#-x402) and [Skyfire](#-skyfire), allowing AI agents to pay for Actor runs without an API token.\n\nApify MCP Server is compatible with `Claude Code, Claude.ai, Cursor, VS Code` and any client that adheres to the Model Context Protocol.\nCheck out the [MCP clients section](#-mcp-clients) for more details or visit the [MCP configuration page](https://mcp.apify.com).\n\n![Apify-MCP-server](https://raw.githubusercontent.com/apify/apify-mcp-server/refs/heads/master/docs/apify-mcp-server.png)\n\n## Table of Contents\n- [🌐 Introducing Apify MCP Server](#-introducing-apify-mcp-server)\n- [🚀 Quickstart](#-quickstart)\n- [🤖 MCP clients](#-mcp-clients)\n- [🪄 Try Apify MCP instantly](#-try-apify-mcp-instantly)\n- [💰 Agentic payments](#-agentic-payments)\n - [How agentic payments work](#how-agentic-payments-work)\n - [💸 x402](#-x402)\n - [🔥 Skyfire](#-skyfire)\n- [🛠️ Tools, resources, and prompts](#%EF%B8%8F-tools-resources-and-prompts)\n- [📊 Telemetry](#-telemetry)\n- [💬 Usage examples](#-usage-examples)\n- [🐛 Troubleshooting](#-troubleshooting)\n- [⚙️ Development](#%EF%B8%8F-development)\n- [🔒 Privacy policy](#-privacy-policy)\n- [🤝 Contributing](#-contributing)\n- [📚 Learn more](#-learn-more)\n\n# 🌐 Introducing Apify MCP Server\n\nThe Apify MCP Server allows an AI assistant to use any [Apify Actor](https://apify.com/store) as a tool to perform a specific task.\nFor example, it can:\n- Use [Facebook Posts Scraper](https://apify.com/apify/facebook-posts-scraper) to extract data from Facebook posts from multiple pages/profiles.\n- Use [Google Maps Email Extractor](https://apify.com/lukaskrivka/google-maps-with-contact-details) to extract contact details from Google Maps.\n- Use [Google Search Results Scraper](https://apify.com/apify/google-search-scraper) to scrape Google Search Engine Results Pages (SERPs).\n- Use [Instagram Scraper](https://apify.com/apify/instagram-scraper) to scrape Instagram posts, profiles, places, photos, and comments.\n- Use [RAG Web Browser](https://apify.com/apify/rag-web-browser) to search the web, scrape the top N URLs, and return their content.\n\n**Video tutorial: Integrate 8,000+ Apify Actors and Agents with Claude**\n\n[![Apify MCP Server Tutorial: Integrate 5,000+ Apify Actors and Agents with Claude](https://img.youtube.com/vi/BKu8H91uCTg/hqdefault.jpg)](https://www.youtube.com/watch?v=BKu8H91uCTg)\n\n# 🚀 Quickstart\n\nYou can use the Apify MCP Server in two ways:\n\n**HTTPS Endpoint (mcp.apify.com)**: Connect from your MCP client via OAuth or by including the `Authorization: Bearer ` 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](https://claude.ai) or [Visual Studio Code](https://code.visualstudio.com/) using just the URL: `https://mcp.apify.com`.\n- `https://mcp.apify.com` streamable transport\n\n**Standard Input/Output (stdio)**: Ideal for local integrations and command-line tools like the Claude for Desktop client.\n- Set the MCP client server command to `npx @apify/actors-mcp-server` and the `APIFY_TOKEN` environment variable to your Apify API token.\n- See `npx @apify/actors-mcp-server --help` for more options.\n\nYou can find detailed instructions for setting up the MCP server in the [Apify documentation](https://docs.apify.com/platform/integrations/mcp).\n\n# 🤖 MCP clients\n\nApify MCP Server is compatible with any MCP client that adheres to the [Model Context Protocol](https://modelcontextprotocol.org/), but the level of support for dynamic tool discovery and other features may vary between clients.\n\n\nTo interact with the Apify MCP Server, you can use clients such as [Claude Desktop](https://claude.ai/download), [Visual Studio Code](https://code.visualstudio.com/), or [Apify Tester MCP Client](https://apify.com/jiri.spilka/tester-mcp-client).\n\nVisit [mcp.apify.com](https://mcp.apify.com) to configure the server for your preferred client.\n\n![Apify-MCP-configuration-clients](https://raw.githubusercontent.com/apify/apify-mcp-server/refs/heads/master/docs/mcp-clients.png)\n\n### Tested clients\n\n- [Claude Desktop](https://docs.apify.com/platform/integrations/claude-desktop)\n- Claude.ai (web)\n- [ChatGPT](https://docs.apify.com/platform/integrations/chatgpt)\n- VS Code (Genie)\n- Cursor\n- OpenCode\n- [Kiro](https://kiro.dev)\n- [Apify Tester MCP Client](https://apify.com/jiri.spilka/tester-mcp-client) — designed for testing Apify MCP servers\n\n\n# 🪄 Try Apify MCP instantly\n\nWant to try Apify MCP without any setup?\n\nCheck out [Apify Tester MCP Client](https://apify.com/jiri.spilka/tester-mcp-client)\n\nThis interactive, chat-like interface provides an easy way to explore the capabilities of Apify MCP without any local setup.\nSign in with your Apify account and start experimenting with web scraping, data extraction, and automation tools!\n\nOr use the MCP bundle file (formerly known as Anthropic Desktop extension file, or DXT) for one-click installation: [Apify MCP Server MCPB file](https://github.com/apify/apify-mcp-server/releases/latest/download/apify-mcp-server.mcpb)\n\n\n# 💰 Agentic payments\n\nYou can pay for Actor runs without an Apify API token using either **x402** or **Skyfire**.\n\n- **x402** pays with USDC on [Base](https://base.org) and does not require a separate platform account. It is fully supported by [`mcpc`](https://github.com/apify/mcp-cli) (`npm install -g @apify/mcpc`). We use `mcpc` because it is one of the few MCP clients that supports the latest features and the x402 protocol natively.\n- **Skyfire** pays with PAY tokens and requires a Skyfire account with a funded wallet. It does not require a special MCP client; the entire payment flow is handled directly through the MCP tool call parameters.\n\n## How agentic payments work\n\nActor run costs vary, so both payment methods use a prepaid balance model. The payment flow happens in four steps:\n\n1. **Discovery**: The agent discovers Actors with `search-actors` or `fetch-actor-details`. Those calls are free.\n2. **Prepayment**: Before running a paid Actor tool, the agent funds a prepaid balance.\n - **x402**: `mcpc` automatically signs a $1.00 USDC transaction.\n - **Skyfire**: The agent creates a PAY token (minimum $5.00) using Skyfire's `create-pay-token` tool.\n3. **Execution**: The agent calls the Actor tool.\n - **x402**: Handled automatically by `mcpc` using the prepaid balance.\n - **Skyfire**: The agent explicitly passes the PAY token in the `skyfire-pay-id` input property.\n4. **Resolution**: The tool returns the Actor results. Unused funds stay available for later runs.\n - **x402**: After 60 minutes of inactivity, the server refunds any unused balance to the wallet on [Base](https://base.org).\n - **Skyfire**: Skyfire returns unused funds when the token expires.\n\n## 💸 x402\n\nThe [x402 protocol](https://www.x402.org/) enables direct, machine-to-machine payments. Your MCP client can use it to pay for Actor runs with USDC on the [Base blockchain](https://base.org/), completely bypassing the need for an Apify API token.\n\n### Prerequisites\n\n- A wallet with USDC on [Base](https://base.org) mainnet.\n\n### Setup\n\nCreate or import a wallet:\n\n```bash\n# Create a new wallet\nmcpc x402 init\n\n# Import an existing wallet\nmcpc x402 import \n\n# Show the wallet address so you can fund it with USDC on Base (https://base.org)\nmcpc x402 info\n```\n\nConnect to the server with x402 enabled:\n\n```bash\nmcpc connect \"mcp.apify.com?payment=x402\" @apify --x402\n```\n\nYou can now call a paid tool:\n\n```bash\nmcpc @apify tools-call call-actor actor:=\"apify/rag-web-browser\" input:='{\"query\": \"latest AI news\"}'\n```\n\n## 🔥 Skyfire\n\n[Skyfire](https://www.skyfire.xyz/) provides managed payment infrastructure for AI agents. Instead of authenticating with an Apify API token, your agent passes a Skyfire payment token to cover the cost of each tool call using PAY tokens.\n\n### Prerequisites\n\n- A [Skyfire account](https://www.skyfire.xyz/) with a funded wallet.\n- An MCP client that supports multiple servers, such as Claude Desktop, OpenCode, or VS Code.\n\n### Setup\n\nConfigure the Skyfire MCP server and the Apify MCP Server in your client. Add `payment=skyfire` to the Apify server URL:\n\n```json\n{\n \"mcpServers\": {\n \"skyfire\": {\n \"url\": \"https://api.skyfire.xyz/mcp/sse\",\n \"headers\": {\n \"skyfire-api-key\": \"\"\n }\n },\n \"apify\": {\n \"url\": \"https://mcp.apify.com?payment=skyfire\"\n }\n }\n}\n```\n\nSee the [Skyfire integration documentation](https://docs.apify.com/platform/integrations/skyfire) for setup details. The [Agentic Payments with Skyfire](https://blog.apify.com/agentic-payments-skyfire/) post provides additional background.\n\n# 🛠️ Tools, resources, and prompts\n\nThe MCP server provides a set of tools for interacting with Apify Actors.\nSince Apify Store is large and growing rapidly, the MCP server provides a way to dynamically discover and use new Actors.\n\n### Actors\n\nAny [Apify Actor](https://apify.com/store) can be used as a tool.\nBy default, the server is pre-configured with one Actor, `apify/rag-web-browser`, and several helper tools.\nThe MCP server loads an Actor's input schema and creates a corresponding MCP tool.\nThis allows the AI agent to know exactly what arguments to pass to the Actor and what to expect in return.\n\n\nFor example, for the `apify/rag-web-browser` Actor, the input parameters are:\n\n```json\n{\n \"query\": \"restaurants in San Francisco\",\n \"maxResults\": 3\n}\n```\nYou don't need to manually specify which Actor to call or its input parameters; the LLM handles this automatically.\nWhen a tool is called, the arguments are automatically passed to the Actor by the LLM.\nYou can refer to the specific Actor's documentation for a list of available arguments.\n\n### Helper tools\n\nOne of the most powerful features of using MCP with Apify is dynamic tool discovery.\nIt allows an AI agent to find new tools (Actors) as needed and incorporate them.\nHere are some special MCP operations and how the Apify MCP Server supports them:\n\n- **Apify Actors**: Search for Actors, view their details, and use them as tools for the AI.\n- **Apify documentation**: Search the Apify documentation and fetch specific documents to provide context to the AI.\n- **Actor runs**: Get lists of your Actor runs, inspect their details, and retrieve logs.\n- **Apify storage**: Access data from your datasets and key-value stores.\n\n### Overview of available tools\n\nHere is an overview list of all the tools provided by the Apify MCP Server.\n\nLegend for the **Enabled by default** column:\n- ✅ — in the default tool set.\n- ⚡ — auto-injected when `call-actor`, `add-actor`, an Actor tool, or `get-actor-run` is present (which is true in the default configuration).\n\n| Tool name | Category | Description | Enabled by default |\n| :--- | :--- | :--- | :---: |\n| `search-actors` | actors | Search for Actors in Apify Store. | ✅ |\n| `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. | ✅ |\n| `call-actor` | actors | Call an Actor and get its run results. Use fetch-actor-details first to get the Actor's input schema. | ✅ |\n| `get-actor-run` | runs | Get detailed information about a specific Actor run. | ⚡ |\n| `get-dataset-items` | storage | Retrieve items from a dataset with support for filtering and pagination. | ⚡ |\n| `get-key-value-store-record`| storage | Get the value associated with a specific key in a key-value store. | ⚡ |\n| `abort-actor-run` | runs | Abort a running Actor run, optionally gracefully. | ⚡ |\n| `search-apify-docs` | docs | Search the Apify documentation for relevant pages. | ✅ |\n| `fetch-apify-docs` | docs | Fetch the full content of an Apify documentation page by its URL. | ✅ |\n| [`apify--rag-web-browser`](https://apify.com/apify/rag-web-browser) | Actor (see [tool configuration](#tools-configuration)) | An Actor tool to browse the web. | ✅ |\n| `get-actor-run-list` | runs | Get a list of an Actor's runs, filterable by status. | |\n| `get-actor-log` | runs | Retrieve the logs for a specific Actor run. | |\n| `get-dataset` | storage | Get metadata about a specific dataset. | |\n| `get-dataset-schema` | storage | Generate a JSON schema from dataset items. | |\n| `get-key-value-store` | storage | Get metadata about a specific key-value store. | |\n| `get-key-value-store-keys`| storage | List the keys within a specific key-value store. | |\n| `get-dataset-list` | storage | List all available datasets for the user. | |\n| `get-key-value-store-list`| storage | List all available key-value stores for the user. | |\n| `add-actor` | experimental | Add an Actor as a new tool for the user to call. | |\n\n> **Note:**\n>\n> When `call-actor`, `add-actor`, an Actor tool, or `get-actor-run` is present, the server auto-injects `get-actor-run`, `get-dataset-items`, `get-key-value-store-record`, and `abort-actor-run`.\n>\n> When you call an Actor — through `call-actor` or directly via an Actor tool (e.g., `apify--rag-web-browser`) — the response contains run metadata, storage IDs, and a `summary` + `nextStep`, but no dataset items. To fetch items, follow `nextStep` and call `get-dataset-items` (auto-injected), passing the `datasetId` returned from the call.\n\n### Tool annotations\n\nAll tools include metadata annotations to help MCP clients and LLMs understand tool behavior:\n\n- **`title`**: Short display name for the tool (e.g., \"Search Actors\", \"Call Actor\", \"apify/rag-web-browser\")\n- **`readOnlyHint`**: `true` for tools that only read data without modifying state (e.g., `get-dataset`, `fetch-actor-details`)\n- **`openWorldHint`**: `true` for tools that access external resources outside the Apify platform (e.g., `call-actor` executes external Actors). Tools that interact only with the Apify platform (like `search-actors` or `fetch-apify-docs`) do not have this hint.\n\n### Tools configuration\n\nThe `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.\n\nWhen no query parameters are provided, the MCP server loads the following `tools` by default:\n\n- `actors`\n- `docs`\n- `apify/rag-web-browser`\n\nIf the tools parameter is specified, only the listed tools or categories will be enabled – no default tools will be included.\n\n> **Easy configuration:**\n>\n> Use the [UI configurator](https://mcp.apify.com/) to configure your server, then copy the configuration to your client.\n\n**Configuring the hosted server:**\n\nThe hosted server can be configured using query parameters in the URL. For example, to load the default tools, use:\n\n```\nhttps://mcp.apify.com?tools=actors,docs,apify/rag-web-browser\n```\n\n\nFor 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:\n\n```\nhttps://mcp.apify.com?tools=apify/my-actor\n```\n\nThis setup exposes only the specified Actor (`apify/my-actor`) as a tool. No other tools will be available.\n\n**Configuring the CLI:**\n\nThe CLI can be configured using command-line flags. For example, to load the same tools as in the hosted server configuration, use:\n\n```bash\nnpx @apify/actors-mcp-server --tools actors,docs,apify/rag-web-browser\n```\n\nThe minimal configuration is similar to the hosted server configuration:\n\n```bash\nnpx @apify/actors-mcp-server --tools apify/my-actor\n```\n\nAs above, this exposes only the specified Actor (`apify/my-actor`) as a tool. No other tools will be available.\n\n> **⚠️ Important recommendation**\n>\n> **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.\n>\n> **For production use and stable interfaces, always explicitly specify the `tools` parameter** to ensure your configuration remains consistent across updates.\n\n### UI mode configuration\n\nThe `ui` parameter enables [MCP Apps](https://mcp.apify.com/) widget rendering in tool responses. When enabled, tools like `search-actors` return interactive MCP App responses.\n\n**Configuring the hosted server:**\n\nEnable UI mode using the `ui` query parameter:\n\n```\nhttps://mcp.apify.com?ui=true\n```\n\nYou can combine it with other parameters:\n\n```\nhttps://mcp.apify.com?tools=actors,docs&ui=true\n```\n\n**Configuring the CLI:**\n\nThe CLI can be configured using command-line flags. For example, to enable UI mode:\n\n```bash\nnpx @apify/actors-mcp-server --ui true\n```\n\nYou can also set it via the `UI_MODE` environment variable:\n\n```bash\nexport UI_MODE=true\nnpx @apify/actors-mcp-server\n```\n\n### Backward compatibility\n\nThe v2 configuration preserves backward compatibility with v1 usage. Notes:\n\n- `actors` param (URL) and `--actors` flag (CLI) are still supported.\n - Internally they are merged into `tools` selectors.\n - Examples: `?actors=apify/rag-web-browser` ≡ `?tools=apify/rag-web-browser`; `--actors apify/rag-web-browser` ≡ `--tools apify/rag-web-browser`.\n- `enable-adding-actors` (CLI) and `enableAddingActors` (URL) are supported but deprecated.\n - Prefer `tools=experimental` or including the specific tool `tools=add-actor`.\n - Behavior remains: when enabled with no `tools` specified, the server exposes only `add-actor`; when categories/tools are selected, `add-actor` is also included.\n- `enableActorAutoLoading` remains as a legacy alias for `enableAddingActors` and is mapped automatically.\n- Defaults remain compatible: when no `tools` are specified, the server loads `actors`, `docs`, and `apify/rag-web-browser`.\n - If any `tools` are specified, the defaults are not added (same as v1 intent for explicit selection).\n- `call-actor` is now included by default via the `actors` category (additive change). To exclude it, specify an explicit `tools` list without `actors`.\n- `preview` category is deprecated and removed. Use specific tool names instead.\n\nExisting URLs and commands using `?actors=...` or `--actors` continue to work unchanged.\n\n### Prompts\n\nThe 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](https://apify.com/apify/rag-web-browser) Actor.\n\n### Resources\n\nThe server does not yet provide any resources.\n\n## 💬 Usage examples\n\nBelow are realistic examples showing how an AI assistant uses the Apify MCP Server tools.\n\n### Example 1: Search the web using RAG Web Browser\n\n**User prompt:**\n> Find the latest news about autonomous AI agents and summarize the key developments.\n\nThe AI assistant calls the pre-configured `apify--rag-web-browser` Actor tool to search the web and return content from top results.\nThe tool returns markdown content from the top 3 search results, which the AI assistant then summarizes for the user.\n\n### Example 2: Discover and run an Actor from Apify Store\n\n**User prompt:**\n> Scrape the top 10 restaurants in Prague from Google Maps with their contact details.\n\nThe AI assistant first searches for a suitable Actor, inspects its input schema, and then executes it.\nThe tool returns a preview of the scraped data including restaurant names, addresses, ratings, phone numbers, and websites.\n\n### Example 3: Retrieve and paginate through Actor run results\n\n**User prompt:**\n> Show me the next 10 results from that scraping run.\n\nThe AI assistant uses the dataset ID from the previous Actor run to fetch additional items.\nExpected output: The tool returns the next page of structured data items from the Actor's output dataset.\n\n## 📡 Telemetry\n\nThe Apify MCP Server collects telemetry data about tool calls to help Apify understand usage patterns and improve the service.\nBy default, telemetry is **enabled** for all tool calls.\n\nThe stdio transport also uses [Sentry](https://sentry.io) for error tracking, which helps us identify and fix issues faster.\nSentry is automatically disabled when telemetry is opted out.\n\n### Opting out of telemetry\n\nYou 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`.\nCLI flags take precedence over environment variables.\n\n#### Examples\n\n**For the remote server (mcp.apify.com)**:\n```text\n# Disable via URL parameter\nhttps://mcp.apify.com?telemetry-enabled=false\n```\n\n**For the local stdio server**:\n```bash\n# Disable via CLI flag\nnpx @apify/actors-mcp-server --telemetry-enabled=false\n\n# Or set environment variable\nexport TELEMETRY_ENABLED=false\nnpx @apify/actors-mcp-server\n```\n\n# ⚙️ Development\n\nPlease see the [CONTRIBUTING.md](https://github.com/apify/apify-mcp-server/blob/master/CONTRIBUTING.md) guide for contribution guidelines and commit message conventions.\n\nFor detailed development setup, project structure, and local testing instructions, see the [DEVELOPMENT.md](https://github.com/apify/apify-mcp-server/blob/master/DEVELOPMENT.md) guide.\n\n## Prerequisites\n\n- [Node.js](https://nodejs.org/en) (v22 or higher)\n\nCreate an environment file, `.env`, with the following content:\n```text\nAPIFY_TOKEN=\"your-apify-token\"\n```\n\nBuild the `actors-mcp-server` package:\n\n```bash\npnpm run build\n```\n\n## Start HTTP streamable MCP server\n\nRun using Apify CLI:\n\n```bash\nexport APIFY_TOKEN=\"your-apify-token\"\nexport APIFY_META_ORIGIN=STANDBY\napify run -p\n```\n\nOnce the server is running, you can use the [MCP Inspector](https://github.com/modelcontextprotocol/inspector) to debug the server exposed at `http://localhost:3001`.\n\n## Start standard input/output (stdio) MCP server\n\nYou can launch the MCP Inspector with this command:\n\n```bash\nexport APIFY_TOKEN=\"your-apify-token\"\nnpx @modelcontextprotocol/inspector node ./dist/stdio.js\n```\n\nUpon launching, the Inspector will display a URL that you can open in your browser to begin debugging.\n\n## Unauthenticated access\n\nWhen the `tools` query parameter includes only tools explicitly enabled for unauthenticated use, the hosted server allows access without an API token.\nCurrently allowed tools: `search-actors`, `fetch-actor-details`, `search-apify-docs`, `fetch-apify-docs`.\nExample: `https://mcp.apify.com?tools=search-actors`.\n\n## 🐦 Canary PR releases\n\nApify MCP is split across two repositories: this repository for core MCP logic and the private `apify-mcp-server-internal` for the hosted server.\nChanges must be synchronized between both.\n\nTo create a canary release, add the `beta` tag to your PR branch.\nThis publishes the package to [pkg.pr.new](https://pkg.pr.new/) for staging and testing before merging.\nSee [the workflow file](https://github.com/apify/apify-mcp-server/blob/master/.github/workflows/pre_release.yaml) for details.\n\n## 🐋 Docker Hub integration\nThe Apify MCP Server is also available on [Docker Hub](https://hub.docker.com/mcp/server/apify-mcp-server/overview), registered via the [mcp-registry](https://github.com/docker/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, 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](https://github.com/docker/mcp-registry/blob/main/CONTRIBUTING.md).\n\n# 🐛 Troubleshooting\n\nFor step-by-step troubleshooting, see the [Claude Desktop integration guide](https://docs.apify.com/platform/integrations/claude-desktop) in the Apify documentation.\n\n## 💡 Limitations\n\nThe Actor input schema is processed to be compatible with most MCP clients while adhering to [JSON Schema](https://json-schema.org/) standards. The processing includes:\n- **Descriptions** are truncated to 500 characters (as defined in `MAX_DESCRIPTION_LENGTH`).\n- **Enum fields** are truncated to a maximum combined length of 2000 characters for all elements (as defined in `ACTOR_ENUM_MAX_LENGTH`).\n- **Required fields** are explicitly marked with a `REQUIRED` prefix in their descriptions for compatibility with frameworks that may not handle the JSON schema properly.\n- **Nested properties** are built for special cases like proxy configuration and request list sources to ensure the correct input structure.\n- **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.\n- **Enum values and examples** are added to property descriptions to ensure visibility, even if the client doesn't fully support the JSON schema.\n- **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 Apify Store—including rental Actors—connect to the hosted endpoint.\n\n# 🔒 Privacy policy\n\nWhen you use this server, your requests and Actor inputs are sent to the Apify API for execution.\nData is not shared with third parties beyond what is necessary to run the requested Actors.\nFor full details on data collection, usage, sharing, and retention, see [Apify Legal](https://docs.apify.com/legal).\n\n# 🤝 Contributing\n\nWe welcome contributions to improve the Apify MCP Server! Here's how you can help:\n\n- **🐛 Report issues**: Find a bug or have a feature request? [Open an issue](https://github.com/apify/apify-mcp-server/issues).\n- **🔧 Submit pull requests**: Fork the repo and submit pull requests with enhancements or fixes.\n- **📚 Documentation**: Improvements to docs and examples are always welcome.\n- **💡 Share use cases**: Contribute examples to help other users.\n\nFor major changes, please open an issue first to discuss your proposal and ensure it aligns with the project's goals.\n\n# 📚 Learn more\n\n- [Model Context Protocol](https://modelcontextprotocol.org/)\n- [What are AI Agents?](https://blog.apify.com/what-are-ai-agents/)\n- [What is MCP and why does it matter?](https://blog.apify.com/what-is-model-context-protocol/)\n- [How to use MCP with Apify Actors](https://blog.apify.com/how-to-use-mcp/)\n- [Tester MCP Client](https://apify.com/jiri.spilka/tester-mcp-client)\n- [Webinar: Building and Monetizing MCP Servers on Apify](https://www.youtube.com/watch?v=w3AH3jIrXXo)\n- [How to build and monetize an AI agent on Apify](https://blog.apify.com/how-to-build-an-ai-agent/)\n- [Connect Apify MCP with Claude Desktop](https://docs.apify.com/platform/integrations/claude-desktop)\n- [Connect Apify MCP with ChatGPT](https://docs.apify.com/platform/integrations/chatgpt)\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays",0,"categoryDisplayName","Data & Analytics","upstream",{"_98":99,"_100":101,"_102":103,"_104":105,"_106":107},"requiredSecrets",[],"tags",[],"assets",[],"stats",{"_96":83,"_97":83},"ratingAvg","reviewCount","topics",[108,109,110,111],"license","MIT","language","TypeScript","registryName","com.apify/apify-mcp-server","homepage","https://mcp.apify.com","agents","ai","mcp","mcp-server","ENV",{"_114":115,"_116":117,"_118":119},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]

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