エージェントを強化
公開元自身のコマンドです。PlugMCP のエンドポイントではありません。
キーとログ付きでコピーをホスト。
npx -y @apify/[email protected]Claude Code にはローカルコマンド用のインストールリンクがありません。下のコマンドをコピーして、ターミナルで実行してください。
claude mcp add apify-actors-mcp-server -- npx -y @apify/actors-mcp-server@0.21.10ターミナルで実行します。
{ "mcpServers": { "apify-actors-mcp-server": { "command": "npx", "args": [ "-y", "@apify/[email protected]" ] } }}claude_desktop_config.json
リモート URL か、手元のマシンのコマンドか 2 つのインストール方法の違い
PlugMCP で実行
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
起動公開元より
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 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!
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.
⚠️ Legacy SSE transport removed. The
https://mcp.apify.com/sseendpoint has been removed in favor of Streamable HTTP. Migrate your client tohttps://mcp.apify.com— drop the/ssesuffix from your configuration.
💰 The server also supports agentic payments: buy a token from AGI to run any Actor, or pay per-request via direct x402 (Pay Per Event Actors only) or Skyfire.
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.
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 8,000+ 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 streamable 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.
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.
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. 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
You can pay for Actor runs without an Apify API token using AGI, direct x402, or Skyfire.
mcp.apify.com and api.apify.com — works for any Actor, not just Pay Per Event ones. Recommended for new integrations; see AGI (recommended) below.mcpc (brew install apify/tap/mcpc or 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.ℹ️ Scope: Both direct x402 and Skyfire are limited to Pay Per Event Actors, don't support Standby Actors, and settle per run instead of minting a token.
Actor run costs vary, so both payment methods use a prepaid balance model. The payment flow happens in four steps:
search-actors or fetch-actor-details. Those calls are free.mcpc automatically signs a $1.00 USDC transaction.create-pay-token tool.mcpc using the prepaid balance.skyfire-pay-id input property.AGI (Apify Agent General Interface) is the recommended way for autonomous agents to pay for Apify usage without an account. Pay once via x402 or MPP, receive a prepaid, spend-capped Apify API token, and use it directly against mcp.apify.com and api.apify.com (Authorization: Bearer <token>) — for any Actor.
Full protocol, supported payment methods, and current terms (minimum amount, token lifetime, refund policy) are documented at agi.apify.com/AGENTS.md — treat it as the single source of truth.
The x402 protocol enables direct, machine-to-machine payments. Your MCP client can use it to pay for Actor runs with USDC on the Base blockchain, completely bypassing the need for an Apify API token.
Create or import a wallet:
# Create a new wallet
mcpc x402 init
# Import an existing wallet
mcpc x402 import <private-key>
# Show the wallet address and a funding QR code, so you can fund it with USDC on Base (https://base.org)
mcpc x402
Connect to the server with x402 enabled:
mcpc connect "mcp.apify.com?payment=x402" @apify --x402
You can now call a paid tool:
mcpc @apify tools-call call-actor actor:="apify/rag-web-browser" input:='{"query": "latest AI news"}'
Skyfire 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.
Configure the Skyfire MCP server and the Apify MCP Server in your client. Add payment=skyfire 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"
}
}
}
See the Skyfire integration documentation for setup details. The Agentic Payments with Skyfire post provides additional background.
The MCP server provides a set of tools for interacting with Apify Actors. Since 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 two Actors, apify/rag-web-browser and apify/web-fetch, 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.
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:
Here is an overview list of all the tools provided by the Apify MCP Server.
Legend for the Enabled by default column:
call-actor, an Actor tool, or get-actor-run is present (which is true in the default configuration).tools= list that omits it. Explicitly selecting it (tools=report-problem or tools=dev) serves it regardless of client — telemetry still must be enabled.Note:
When
call-actor, an Actor tool, orget-actor-runis present, the server auto-injectsget-actor-run,get-dataset-items,get-key-value-store-record, andabort-actor-run.When you call an Actor — through
call-actoror directly via an Actor tool (e.g.,apify--rag-web-browser) — the response contains run metadata, storage IDs, and asummary+nextStep, but no dataset items. To fetch items, follownextStepand callget-dataset-items(auto-injected), passing thedatasetIdreturned from the call.
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). Actor tools get it only for Apify-maintained Actors with limited permissions and no accepted secret, writable storage, MCP connector, or code input (e.g., apify--web-fetch). Direct tools can restrict the original Actor inputs: apify--website-content-crawler excludes and rejects pageFunction and initialCookies, so it is read-only. Other Actor tools are marked destructive.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.The direct apify--website-content-crawler tool defaults respectRobotsTxtFile to true; an explicit false is preserved. These restrictions and defaults apply only to the direct tool. call-actor, call-actor-widget, and fetch-actor-details retain the original Actor input contract.
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=call-actor loads just one tool.
When no query parameters are provided, the MCP server loads the following tools by default:
actorsdocsapify/rag-web-browserapify/web-fetchIf the tools parameter is specified, only the listed tools or categories will be enabled – no default tools will be included.
report-problem is served by default (subject to the gating in the footnote above) but lives in the dev category, so an explicit tools=dev selects it too. To disable it, pass an explicit tools= list that omits it (e.g. tools=actors,docs).
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,apify/web-fetch
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,apify/web-fetch
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
toolsparameter 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
toolsparameter to ensure your configuration remains consistent across updates.
The ui parameter enables MCP Apps widget rendering in tool responses. When enabled, tools like search-actors return interactive MCP App responses.
Configuring the hosted server:
Enable UI mode using the ui query parameter:
https://mcp.apify.com?ui=true
You can combine it with other parameters:
https://mcp.apify.com?tools=actors,docs&ui=true
Configuring the CLI:
The CLI can be configured using command-line flags. For example, to enable UI mode:
npx @apify/actors-mcp-server --ui true
You can also set it via the UI_MODE environment variable:
export UI_MODE=true
npx @apify/actors-mcp-server
The v2 configuration preserves backward compatibility with v1 usage. Notes:
actors param (URL) and --actors flag (CLI) are still supported.
tools selectors.?actors=apify/rag-web-browser ≡ ?tools=apify/rag-web-browser; --actors apify/rag-web-browser ≡ --tools apify/rag-web-browser.enableAddingActors (URL), enable-adding-actors (CLI), and the legacy enableActorAutoLoading alias have been removed. To call Actors dynamically, use tools=call-actor (included by default via the actors category). Any lingering raw value is ignored.tools are specified, the server loads actors, docs, apify/rag-web-browser, and apify/web-fetch.
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.tools=add-actor, tools=experimental, and tools=preview are retired: they are ignored and load no tools. Use tools=call-actor (or the default actors category) instead.tools=get-actor-log is retired: the tool was renamed to get-actor-run-log. The old selector is ignored and loads no tools. Use tools=get-actor-run-log (or the runs category) instead.tools=source is retired: the category was renamed to versions. The old selector is ignored and loads no tools. Use tools=versions instead.Existing URLs and commands using ?actors=... or --actors continue to work unchanged.
The server advertises the prompts capability, but no prompts are currently registered — prompts/list returns an empty list.
Your Apify data is not enumerated in resources/list — reads are on demand: pass any Apify API GET URL (https://api.apify.com/v2/...) to resources/read and the server injects the session's Apify token and returns the response body. resources/templates/list enumerates the common shapes — dataset items, key-value store records and keys, run metadata, run log — with their paging parameters. Responses inline up to 256 KB; anything larger returns a short notice with a download URL instead of the body. API reads require an Apify token, so a payment-only session (x402 or Skyfire) gets a JSON-RPC error for them.
Below are realistic examples showing how an AI assistant uses the Apify MCP Server tools.
User prompt:
Find the latest news about autonomous AI agents and summarize the key developments.
The AI assistant calls the pre-configured apify--rag-web-browser Actor tool to search the web and return content from top results.
The tool returns markdown content from the top 3 search results, which the AI assistant then summarizes for the user.
User prompt:
Scrape the top 10 restaurants in Prague from Google Maps with their contact details.
The AI assistant first searches for a suitable Actor, inspects its input schema, and then executes it. The tool returns a preview of the scraped data including restaurant names, addresses, ratings, phone numbers, and websites.
User prompt:
Show me the next 10 results from that scraping run.
The AI assistant uses the dataset ID from the previous Actor run to fetch additional items. Expected output: The tool returns the next page of structured data items from the Actor's output dataset.
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.
With telemetry enabled, Actor searches in the Apify Store send the MCP session id as _traceId, and tool-call events carry it as trace_id.
search-actors also takes an optional useCase: one sentence, written by the model, on why the user is searching. It is sent with the Store search as _useCase, and Apify records it in its Store search analytics to improve search results.
Opting out of telemetry disables all of this.
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.
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.
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
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.
Create an environment file, .env, with the following content:
APIFY_TOKEN="your-apify-token"
Build the actors-mcp-server package:
pnpm run build
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.
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.
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, apify-api-search, apify-api-details.
Example: https://mcp.apify.com?tools=search-actors.
Apify MCP is split across two repositories: this repository 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 label to your pull request.
This publishes the package to pkg.pr.new for staging and testing before merging.
See the workflow file for details.
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, 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.
For step-by-step troubleshooting, see the Claude Desktop integration guide in the Apify documentation.
The Actor input schema is processed to be compatible with most MCP clients while adhering to JSON Schema standards. The processing includes:
ACTOR_MAX_DESCRIPTION_LENGTH) are cut at the last complete sentence in that window, or the last complete word if there is no sentence break, and marked [Description truncated].ACTOR_ENUM_MAX_LENGTH) are kept in full; larger enums are omitted, with a short note and a few example values in the description instead.REQUIRED prefix in their descriptions for compatibility with frameworks that may not handle the JSON schema properly.When you use this server, your requests and Actor inputs are sent to the Apify API for execution. Data is not shared with third parties beyond what is necessary to run the requested Actors. For full details on data collection, usage, sharing, and retention, see Apify Legal.
We welcome bug reports, feature requests, and documentation fixes. Send us the problem, not the patch — a precise issue with a reproduction is more useful than a pull request.
Full rules, including AI-assisted contributions: CONTRIBUTING.md.
{ "mcpServers": { "apify-actors-mcp-server": { "command": "npx", "args": [ "-y", "@apify/[email protected]" ] } }}ツール
ツール一覧はまだありません。リモートサーバーは、最初のヘルスチェック後に取得されます。
よくある質問
このページで Cursor、Claude Desktop、Claude Code、VS Code 用の stdio 設定をコピーできます。PlugMCP のプロキシではなく、公開元のインストール方法を使います。
このレコードに必須の環境変数は記載されていません。
@apify/actors は stdio 経由でローカル実行されます。
最終チェックは 2026-11-15 です。 Apify MCP Server