How can the AI agent run a scrape on a list of product URLs?

First, find your specific scraping Actor ID via `list_actors`. Then, prompt your agent to execute `run_actor`, providing the target URLs formatted as a structured JSON input payload. It returns a 'Run ID'. You can poll this run via `get_run`, and once it succeeds, the agent calls `get_dataset_items` to pull all acquired data straight to your window.

Can the agent interact with run configurations mid-way during crawling?

Yes. If an Apify crawler is currently executing and utilizes a Request Queue, you can instruct your agent to call `push_to_queue`. Doing so dynamically ships new URLs to the active queue instance, extending the current web crawl without needing to stop or restart the Actor.

Can my AI automatically detect scraping timeouts and debug the failure?

Absolutely. Because your agent can track real execution flows with `get_run`, it's aware if it transitions to TIMED-OUT or FAILED states. Subsequently, you can ask the agent to examine the KV Store log outputs ensuring the underlying issue (e.g. captcha block, blocking proxy) is identified immediately.

Apify MCP Server for VS Code Copilot 10 tools — connect in under 2 minutes

Name: Apify
Availability: InStock
Author: Vinkius

code.visualstudio.com/docs/copilot/chat/mcp-servers

Built by Vinkius GDPR 10 Tools IDE

GitHub Copilot in VS Code is the most widely adopted AI coding assistant, embedded directly into the world's most popular code editor. With MCP support in Agent mode, Copilot can access external data and APIs to generate context-aware code grounded in real-time information.

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The modern way to manage MCP Servers — no config files, no terminal commands. Install Apify and 2,500+ MCP Servers from a single visual interface.

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Classic Setup·json

{
  "mcpServers": {
    "apify": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Apify MCP Server

Connect your Apify workspace to your AI agent and seamlessly direct full-stack web scraping and data extraction workflows through natural conversation.

GitHub Copilot Agent mode brings Apify data directly into your VS Code workflow. With a project-scoped config, the entire team shares access to 10 tools. Copilot queries live data, generates typed code, and writes tests from actual API responses, all without leaving the editor.

What you can do

Discover & Run Actors — Browse all scraper bots (Actors) available in your account. Fire them off asynchronously or synchronously for fast, targeted scraping
Extract Datasets — Pull robust structured data formats out of completed runs. Retrieve detailed JSON records directly into the agent's context window
Fetch Key-Value Stores — Programmatically read snapshots, cached HTML pages, or screenshots from the Apify Key-Value repositories mapped to a run
Job Control & Scalability — Stop hanging scraper jobs, queue new dynamic URLs mid-run, or inspect deep usage analytics, compute units, and webhooks limits

The Apify MCP Server exposes 10 tools through the Vinkius. Connect it to VS Code Copilot in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Apify to VS Code Copilot via MCP

Follow these steps to integrate the Apify MCP Server with VS Code Copilot.

Create MCP config

Create a .vscode/mcp.json file in your project root

Add the server config

Paste the JSON configuration above

Enable Agent mode

Open GitHub Copilot Chat and switch to Agent mode using the dropdown

Start using Apify

Ask Copilot: "Using Apify, help me...". 10 tools available

Why Use VS Code Copilot with the Apify MCP Server

GitHub Copilot for Visual Studio Code provides unique advantages when paired with Apify through the Model Context Protocol.

VS Code is used by over 70% of developers. adding MCP tools to Copilot means your team can leverage external data without leaving their primary editor

Project-scoped MCP configs (`.vscode/mcp.json`) let you commit server configurations to your repository, ensuring the entire team shares the same tool access

Copilot's Agent mode integrates MCP tools seamlessly with file editing, terminal commands, and workspace search in a single agentic loop

GitHub's enterprise compliance and audit features extend to MCP tool usage, providing visibility into how AI interacts with external services

Apify + VS Code Copilot Use Cases

Practical scenarios where VS Code Copilot combined with the Apify MCP Server delivers measurable value.

Live API integration: Copilot can query an MCP server, inspect the response schema, and generate typed API client code in the same step

DevSecOps workflows: security teams can give developers access to domain intelligence tools directly in their editor for real-time vulnerability assessment during code review

Data pipeline development: Copilot fetches sample data via MCP and generates transformation scripts, validators, and test fixtures from actual API responses

Documentation generation: Copilot queries available tools and auto-generates README sections, API reference docs, and usage examples

Apify MCP Tools for VS Code Copilot (10)

These 10 tools become available when you connect Apify to VS Code Copilot via MCP:

abort_run

Any data already scraped and pushed to the dataset is preserved. The run status changes to ABORTED. Use this to stop runaway scrapes or when sufficient data has been collected. Graceful shutdown depends on the actor implementation. Abort an active Apify actor run

get_account_limits

Essential for monitoring consumption and avoiding overage charges. Check Apify account subscription limits and compute unit usage

get_dataset_items

The datasetId is found in the run object (defaultDatasetId). Supports pagination via limit (max items per page) and offset (starting position). Returns an array of JSON objects containing the scraped data fields. Use limit=1000 for bulk downloads. Export structured JSON data from an Apify dataset

get_key_value_store

Key-value stores hold arbitrary data like screenshots (OUTPUT), configuration files, or intermediate results. The storeId comes from the run object (defaultKeyValueStoreId). Common keys include "OUTPUT", "INPUT", and "SCREENSHOT". Retrieve an item from an Apify actor key-value store

get_run

Poll this endpoint to track long-running scrapes. Check the status and metadata of a specific Apify actor run

list_actors

Includes owned actors and those from the Apify Store that have been saved. Each entry contains the actorId, name, description, and default run configuration. Use the actorId to trigger runs. List all accessible actors in the Apify account

list_webhooks

RUN.SUCCEEDED, ACTOR.RUN.FAILED), target URLs, and associated actor IDs. Webhooks enable event-driven architectures by notifying external systems when actor runs complete or fail. List all configured webhooks in the Apify account

push_to_queue

Pass the queueId (from the run object) and a JSON string array of request objects, e.g., [{"url":"https://...","uniqueKey":"..."}]. This enables dynamic crawling where new pages are discovered and added during execution. Dynamically push new URLs to an active Apify request queue

run_actor

Pass the actorId (e.g., "apify/web-scraper" or a custom ID) and a JSON string with the input configuration (start URLs, proxy settings, max pages, etc.). Returns immediately with a runId. Use ap.get_run to poll for completion and ap.get_dataset_items to retrieve extracted data. Start an Apify actor asynchronously with custom JSON input

run_actor_sync

run_actor but waits for the actor to finish before returning. The response includes the full run object with defaultDatasetId for immediate data retrieval. Best for short-lived actors (under 5 minutes). For long-running scrapes, use the async ap.run_actor instead. Run an Apify actor and block until completion (synchronous)

Example Prompts for Apify in VS Code Copilot

Ready-to-use prompts you can give your VS Code Copilot agent to start working with Apify immediately.

"List all the Apify actors available on my account."

"Verify the status of run 'qKpwH9LgC3r0Xm' and show me its final dataset if finished."

"How are our compute usage limits tracking this current month on Apify?"

Troubleshooting Apify MCP Server with VS Code Copilot

Common issues when connecting Apify to VS Code Copilot through the Vinkius, and how to resolve them.

MCP tools not available

Ensure you are in Agent mode in Copilot Chat. MCP tools only appear in Agent mode.

Apify + VS Code Copilot FAQ

Common questions about integrating Apify MCP Server with VS Code Copilot.

Which VS Code version supports MCP?

MCP support requires VS Code 1.99 or later with the GitHub Copilot extension. Ensure both are updated to the latest version. Older versions of Copilot may not expose the Agent mode toggle.

How do I switch to Agent mode?

Open the Copilot Chat panel and look for two mode options: "Ask" and "Agent". Click "Agent" to enable autonomous tool calling. In Ask mode, Copilot provides conversational answers but cannot invoke MCP tools.

Can I restrict which MCP tools Copilot can access?

Yes. VS Code shows a tool consent dialog before any MCP tool is invoked for the first time. You can also configure tool access policies at the organization level through GitHub Copilot settings.

Does MCP work in VS Code Remote or Codespaces?

Yes. MCP servers configured via .vscode/mcp.json work in Remote SSH, WSL, and GitHub Codespaces environments. The MCP connection is established from the remote host, so ensure the server URL is accessible from that environment.