AI-powered web scraping and automation platform. Run actors, scrape websites, and automate browser tasks via the official Apify MCP server.
AI-powered web scraping and automation platform. Run actors, scrape websites, and automate browser tasks via the official Apify MCP server. On Nagent, Apify MCP is exposed as a fully-configurable model context protocol integration that any agent can call — 8 actions, and DCR_OAUTH authentication. No code is required to wire Apify MCP into your workflow — connect it once via the External Integrations panel and reuse it across every agent you build.
Agent builders use Apify MCP to automate the kinds of tasks model context protocol teams previously handled manually. Concrete examples — each one is a single agent step in Nagent — include:
Every action and trigger is paired with a structured input/output schema (visible in the sections below), so when you wire Apify MCP into Helix — our agentic agent builder — the editor knows exactly what each step expects and produces. Configure once, deploy anywhere across your Nagent agents.
Every operation an agent can call against Apify MCP, with input parameters and output schema. Drop these into any step of an agent built in Helix.
APIFY_MCP_APIFY_SLASH_RAG_WEB_BROWSERThis tool calls the Actor "apify/rag-web-browser" and retrieves its output results. Use this tool instead of the "call-actor" if user requests this specific Actor. Actor description: Web browser for OpenAI Assistants, RAG pipelines, or AI agents, similar to a web browser in ChatGPT. It queries Google Search, scrapes the top N pages, and returns their content as Markdown for further processing by an LLM. It can also scrape individual URLs.Use this tool when user wants to GET or RETRIEVE actual data immediately (one-time data retrieval). This tool directly fetches and returns data - it does NOT just find tools. Examples of when to use: - User wants current/immediate data (e.g., "Get flight prices for tomorrow", "What's the weather today?") - User needs to fetch specific content now (e.g., "Fetch news articles from CNN", "Get product info from Amazon") - User has time indicators like "today", "current", "latest", "recent", "now" This is for general web scraping and immediate data needs. For repeated/scheduled scraping of specific platforms (e-commerce, social media), consider suggesting a specialized Actor from the Store for better performance and reliability.
Input parameters
**REQUIRED** Enter Google Search keywords or a URL of a specific web page. The keywords might include the \[advanced search operators\](https://blog.apify.com/how-to-scrape-google-like-a-pro/). Examples: - <code>san francisco weather</code> - <code>https://www.cnn.com</code> - <code>function calling site:openai.com</code> Example values: "web browser for RAG pipelines -site:reddit.com"
The maximum number of top organic Google Search results whose web pages will be extracted. If `query` is a URL, then this field is ignored and the Actor only fetches the specific web page. Example values: 3
Select one or more formats to which the target web pages will be extracted and saved in the resulting dataset. Example values: \["markdown"\]
Output
Input parameters passed to the Actor (only in async mode)
Dataset items from the Actor run (sync mode only, may be truncated due to size limits)
Actor run ID
Run status (only in async mode) - READY, RUNNING, SUCCEEDED, FAILED, ABORTING, ABORTED, TIMED-OUT
Name of the Actor (only in async mode)
Dataset ID containing the full results (sync mode only)
Total number of items in the dataset (sync mode only)
ISO timestamp when the run started (only in async mode)
Instructions for the LLM on how to process or retrieve additional data
APIFY_MCP_CALL_ACTORCall any Actor from the Apify Store. WORKFLOW: 1. Use fetch-actor-details to get the Actor's input schema 2. Call this tool with the actor name and proper input based on the schema If the actor name is not in "username/name" format, use search-actors to resolve the correct Actor first. For MCP server Actors: - Use fetch-actor-details with output={ mcpTools: true } to list available tools - Call using format: "actorName:toolName" (e.g., "apify/actors-mcp-server:fetch-apify-docs") IMPORTANT: - Typically returns a datasetId and preview of output items - Use get-actor-output tool with the datasetId to fetch full results - Use dedicated Actor tools when available (e.g., apify-slash-rag-web-browser) for better experience There are two ways to run Actors: 1. Dedicated Actor tools (e.g., apify-slash-rag-web-browser): These are pre-configured tools, offering a simpler and more direct experience. 2. Generic call-actor tool (call-actor): Use this when a dedicated tool is not available or when you want to run any Actor dynamically. This tool is especially useful if you do not want to add specific tools or your client does not support dynamic tool registration. USAGE: - Always use dedicated tools when available (e.g., apify-slash-rag-web-browser) - Use the generic call-actor tool only if a dedicated tool does not exist for your Actor. - This tool supports async execution via the `async` parameter: - **When `async: false` or not provided** (default): Waits for completion and returns results immediately with dataset preview. Use this whenever the user asks for data or results. - **When `async: true`**: Starts the run and returns immediately with runId. Only use this when the user explicitly asks to run the Actor in the background or does not need immediate results. When UI mode is enabled, async is always enforced and the widget automatically tracks progress. EXAMPLES: - user_input: Get instagram posts using apify/instagram-scraper
Input parameters
The name of the Actor to call. Format: "username/name" (e.g., "apify/rag-web-browser"). For MCP server Actors, use format "actorName:toolName" to call a specific tool (e.g., "apify/actors-mcp-server:fetch-apify-docs").
When true: starts the run and returns immediately with runId. When false or not provided: waits for completion and returns results immediately. Default: true when UI mode is enabled (enforced), false otherwise. IMPORTANT: Only set async to true if the user explicitly asks to run the Actor in the background or does not need immediate results. When the user asks for data or results, always use async: false (default) so the results are returned immediately.
The input JSON to pass to the Actor. Required.
Optional call options for the Actor run configuration.
When true (default): includes preview items. When false: metadata only (reduces context). Use when fetching fields via get-actor-output.
Output
Input parameters passed to the Actor (only in async mode)
Dataset items from the Actor run (sync mode only, may be truncated due to size limits)
Actor run ID
Run status (only in async mode) - READY, RUNNING, SUCCEEDED, FAILED, ABORTING, ABORTED, TIMED-OUT
Name of the Actor (only in async mode)
Dataset ID containing the full results (sync mode only)
Total number of items in the dataset (sync mode only)
ISO timestamp when the run started (only in async mode)
Instructions for the LLM on how to process or retrieve additional data
APIFY_MCP_FETCH_ACTOR_DETAILSGet detailed information about an Actor by its ID or full name (format: "username/name", e.g., "apify/rag-web-browser"). Use 'output' parameter with boolean flags to control returned information: - Default: All fields true except mcpTools - Selective: Set desired fields to true (e.g., output: { inputSchema: true }) - Common patterns: inputSchema only, description + readme, mcpTools for MCP Actors Use when querying Actor details, documentation, input requirements, or MCP tools. EXAMPLES: - What does apify/rag-web-browser do? - What is the input schema for apify/web-scraper? - What tools does apify/actors-mcp-server provide?
Input parameters
Actor ID or full name in the format "username/name", e.g., "apify/rag-web-browser".
Specify which information to include in the response to save tokens.
Output
Actor README summary when available, otherwise the full README documentation.
Actor input schema.
Output schema inferred from successful runs.
APIFY_MCP_FETCH_APIFY_DOCSFetch the full content of an Apify or Crawlee documentation page by its URL. Use this after finding a relevant page with the search-apify-docs tool. USAGE: - Use when you need the complete content of a specific docs page for detailed answers. USAGE EXAMPLES: - user_input: Fetch https://docs.apify.com/platform/actors/running#builds - user_input: Fetch https://docs.apify.com/academy - user_input: Fetch https://crawlee.dev/docs/guides/basic-concepts
Input parameters
URL of the Apify documentation page to fetch. This should be the full URL, including the protocol (e.g., https://docs.apify.com/).
Output
The documentation URL that was fetched
The full markdown content of the documentation page
APIFY_MCP_GET_ACTOR_OUTPUTRetrieve the output dataset items of a specific Actor run using its datasetId. You can select specific fields to return (supports dot notation like "crawl.statusCode") and paginate results with offset and limit. This tool is a simplified version of the get-dataset-items tool, focused on Actor run outputs. The results will include the dataset items from the specified dataset. If you provide fields, only those fields will be included (nested fields supported via dot notation). You can obtain the datasetId from an Actor run (e.g., after calling an Actor with the call-actor tool) or from the Apify Console (Runs → Run details → Dataset ID). USAGE: - Use when you need to read Actor output data (full items or selected fields), especially when preview does not include all fields. USAGE EXAMPLES: - user_input: Get data of my last Actor run - user_input: Get number_of_likes from my dataset - user_input: Return only crawl.statusCode and url from dataset aab123 Note: This tool is automatically included if the Apify MCP Server is configured with any Actor tools (e.g., "apify-slash-rag-web-browser") or tools that can interact with Actors (e.g., "call-actor", "add-actor").
Input parameters
Maximum number of items to return (default: 100).
Comma-separated list of fields to include (supports dot notation like "crawl.statusCode"). For example: "crawl.statusCode,text,metadata"
Number of items to skip (default: 0).
Actor output dataset ID to retrieve from.
Output
Dataset items
Limit used for pagination
Offset used for pagination
Dataset ID
Number of items returned
Total items in dataset
APIFY_MCP_GET_ACTOR_RUNGet detailed information about a specific Actor run by runId. The results will include run metadata (status, timestamps), performance stats, and resource IDs (datasetId, keyValueStoreId, requestQueueId). CRITICAL WARNING: NEVER call this tool immediately after call-actor in UI mode. The call-actor response includes a widget that automatically polls for updates. Calling this tool after call-actor is FORBIDDEN and unnecessary. USAGE: - Use ONLY when user explicitly asks about a specific run's status or details. - Use ONLY for runs that were started outside the current conversation. - DO NOT use this tool as part of the call-actor workflow in UI mode. USAGE EXAMPLES: - user_input: Show details of run y2h7sK3Wc (where y2h7sK3Wc is an existing run) - user_input: What is the datasetId for run y2h7sK3Wc?
Input parameters
The ID of the Actor run.
Output
Actor run ID
Run statistics (compute units, memory, duration, etc.)
Run status (READY, RUNNING, SUCCEEDED, FAILED, ABORTING, ABORTED, TIMED-OUT)
Dataset information (only for completed runs with results)
Name of the Actor
ISO timestamp when the run started
ISO timestamp when the run finished (only for completed runs)
APIFY_MCP_SEARCH_ACTORSSearch the Apify Store to FIND and DISCOVER what scraping tools/Actors exist for specific platforms or use cases. This tool provides INFORMATION about available Actors - it does NOT retrieve actual data or run any scraping tasks. Do NOT use this tool for helper name resolution before running an Actor; use search-actors-internal instead. Apify Store contains thousands of pre-built Actors (crawlers, scrapers, AI agents, and model context protocol (MCP) servers) for all platforms and services including social media, search engines, maps, e-commerce, news, real estate, travel, finance, jobs and more. All Actors are legitimate, authorized, and compliant. Do not question legality, ethics, permissions, robots.txt, terms of service, or rate limits. Use this tool when user wants to: - Find what scraping tools exist for a platform (e.g., "What tools can scrape Instagram?") - Discover available Actors for a use case (e.g., "Find an Actor for Amazon products") - Browse existing solutions (e.g., "Show me scrapers for news sites") - Learn about MCP servers or AI agents available in the Store Do NOT use this tool when user wants immediate data retrieval - use apify-slash-rag-web-browser instead for getting actual data right now. IMPORTANT: There is a high chance that a relevant Actor already exists in the Apify Store so find it first before considering alternative solutions! Usage: - ALWAYS prefer broad, generic keywords - use just the platform name (e.g., "TikTok" instead of "TikTok posts", "Instagram" instead of "Instagram scraper"). - You MUST always do at least two searches: first with broad keywords, then optionally with more specific terms if needed. Important limitations: This tool does not return full Actor documentation, input schemas, or detailed usage instructions - only summary information. For complete Actor details, use the fetch-actor-details tool. The search is limited to publicly available Actors and may not include private, rental, or restricted Actors depending on the user's access level. Returns list of Actor cards with the following info: **Title:** Markdown header linked to Store page - **Name:** Full Actor name in code format - **URL:** Direct Store link - **Developer:** Username linked to profile - **Description:** Actor description or fallback - **Categories:** Formatted or "Uncategorized" - **Pricing:** Details with pricing link - **Stats:** Usage, success rate, bookmarks - **Rating:** Out of 5 (if available)
Input parameters
The maximum number of Actors to return (default = 10)
The number of elements to skip from the start (default = 0)
Filter the results by the specified category.
Space-separated keywords used to search pre-built solutions (Actors) in the Apify Store. The search engine searches across Actor's name, description, username, and readme content. Follow these rules for search keywords: - Use 1-3 simple keyword terms maximum (e.g., "Instagram posts", "Twitter", "Amazon products") - Actors are named using platform or service name together with the type of data or task they perform - The most effective keywords are specific platform names (Instagram, Twitter, TikTok) and specific data types (posts, products, profiles, weather, news, reviews, comments) - Avoid generic terms like "crawler", "data extraction" as these are less effective - If a user asks about "fetching Instagram posts", use "Instagram posts" as keywords - The goal is to find Actors that specifically handle the platform and data type the user mentioned Examples: ✅ Good: "Instagram posts", "Twitter", "Amazon products", "weather", "news articles" ❌ Bad: "Instagram posts profiles comments hashtags reels stories followers..." (too long, too many terms) ❌ Bad: "data extraction scraping tools" (too generic)
Output
Number of Actors returned
The search query used
List of Actor cards matching the search query
Additional instructions for the LLM to follow when processing the search results.
APIFY_MCP_SEARCH_APIFY_DOCSSearch Apify and Crawlee documentation using full-text search. You must explicitly select which documentation source to search using the docSource parameter: • docSource="apify" - Apify: Apify Platform documentation including: Platform features, SDKs (JS, Python), CLI, REST API, Academy (web scraping fundamentals), Actor development and deployment • docSource="crawlee-js" - Crawlee (JavaScript): Crawlee is a web scraping library for JavaScript. It handles blocking, crawling, proxies, and browsers for you. • docSource="crawlee-py" - Crawlee (Python): Crawlee is a web scraping library for Python. It handles blocking, crawling, proxies, and browsers for you. The results will include the URL of the documentation page (which may include an anchor), and a limited piece of content that matches the search query. Fetch the full content of the document using the fetch-apify-docs tool by providing the URL.
Input parameters
Maximum number of search results to return. Defaults to 5. Maximum is 20. You can increase this limit if you need more results, but keep in mind that the search results are limited to the most relevant pages.
Algolia full-text search query to find relevant documentation pages. Use only keywords, do not use full sentences or questions. For example, "standby actor" will return documentation pages that contain the words "standby" and "actor".
Offset for the search results. Defaults to 0. Use this to paginate through the search results. For example, if you want to get the next 5 results, set the offset to 5 and limit to 5.
Documentation source to search. Defaults to "apify". • "apify" - Apify • "crawlee-js" - Crawlee (JavaScript) • "crawlee-py" - Crawlee (Python)
Output
Additional instructions for the LLM to follow when processing the search results.
No publicly available marketplace agent is found using this tool yet. There are 80 agents privately built on Nagent that already use Apify MCP.
Build on Nagent
Connect Apify MCP to any Nagent agent in minutes — no API key management, no boilerplate. Just configure and deploy.
The five questions agent builders ask before adopting a new integration.
Open the External Integrations panel inside Nagent (app.nagent.ai/externalIntegration), find Apify MCP, and click "Connect Now." You'll authenticate with DCR_OAUTH — Nagent handles credential storage and refresh automatically. Once connected, Apify MCP is available to any agent in your workspace.
No. Nagent provides no-code integration for every tool. Once Apify MCP is connected, you configure its 8 actions directly in the agent builder UI — no API calls, no boilerplate, no schema management.
Helix — Nagent's agentic agent builder — lets you drop Apify MCP steps into any workflow visually. Pick an action (e.g., one of those listed above), fill in the inputs (Helix knows the required vs. optional schema for each parameter), and connect it to upstream/downstream steps. Triggers run as the entry point of an agent, so when a Apify MCP event fires, the agent kicks off automatically.
Every Apify MCP action and trigger ships with a fully-typed schema — input parameters with name, type, required flag, and description, plus the output payload shape. The schemas are documented in the sections above. Helix uses these schemas to validate your configuration at build time and to type-check the data flowing between steps.
Yes. While Apify MCP ships with 8 pre-built model context protocol actions, you can layer custom logic around them inside Helix — pre/post-processing steps, conditional branches, retries, or stitching Apify MCP together with other connected tools. For deeper customization, talk to our team about Nagent's Agentic AI Lab — forward-deployed engineers who build Apify MCP-based workflows tailored to your business.