Apify MCP

Live

BEARER TOKEN

AI

AI

Apify MCP gives agents authenticated access to web scraping: run Actors, extract data, and automate browsers without exposing tokens.

  • Per-user credentials: each call uses the actual user's token, never a shared bot.
  • Encrypted per-tenant vault: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: pre-call scope check, 90-day SIEM-exportable audit chain.
Apify MCP
agent · Acme Q3
Run
Abort Actor Run in Apify MCP
S
apifymcp_abort_actor_run
85ms
Apify MCP agent
Abort an actor run that is currently starting or running. has no effect on runs that are already finished, failed, or ti.
Sources: Apify MCP
apifymcpmcp
1 tool call
18:29
Message Claude...

Apify MCP tools for AI agents

CALL ANY TOOL
10 tools covering call, abort, fetch.
apifymcp_abort_actor_run
Abort an actor run that is currently starting or running. ha
Abort an actor run that is currently starting or running. has no effect on runs that are already finished, failed, or timed out.
Parameters
Name
Type
Required
Description
runId
string
Required
The ID of the Actor run to abort.
gracefully
boolean
Optional
If true, the Actor run will abort gracefully with a 30-second timeout.
apifymcp_call_actor
Call any actor from the apify store. by default waits for co
apifymcp_fetch_actor_details
Get detailed information about an actor by its id or full na
apifymcp_fetch_apify_docs
Fetch the full content of an apify or crawlee documentation
apifymcp_get_actor_run
Get detailed information about a specific actor run by runid
apifymcp_get_dataset_items
Retrieve items from a dataset with pagination, field selecti
apifymcp_get_key_value_store_record
Retrieve a record (json, text, or binary) from a key-value s
apifymcp_rag_web_browser
Web browser for ai agents and rag pipelines. queries google
apifymcp_search_actors
Search the apify store to find and discover what scraping to
apifymcp_search_apify_docs
Search apify and crawlee documentation using full-text searc
Build your Agent
Same auth pattern across every framework.
Python · LlamaIndex
from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="apifymcp")

mcp = MultiServerMCPClient({
"apifymcp": {
"url": "https://mcp.scalekit.com/apifymcp",
"headers": {"Authorization": "Bearer " + token}
}
})
tools = await mcp.get_tools()
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "apifymcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/apifymcp
// Pass: Authorization: Bearer + token
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "apifymcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/apifymcp
// Pass: Authorization: Bearer + token
from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="apifymcp")
# Connect to MCP at https://mcp.scalekit.com/apifymcp
# Pass: Authorization: Bearer + token
Try these prompts
Paste any prompt into your agent to get started.
Get started
Copy the prompt
Copied
Retrieve a record (JSON, text, or binary) from a key-value store by its key?
Copy the prompt
Copied
Abort an Actor run that is currently starting or running?
Advanced
Copy the prompt
Copied
Get detailed information about an Actor by its ID or full name (format: ‘username/name’, e.g?
Copy the prompt
Copied
Search the Apify Store to FIND and DISCOVER what scraping tools/Actors exist for specific platforms or use cases?
SEE HOW AUTH WORKS
User authorises once. Every agent call after uses their token with scope enforcement.
1
Authorize
Your user connects
Apify MCP
once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access
Who:
user ‘A’
when:
Once per user
access:
Limited to user
2
Store
Their
Apify MCP
token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection
vault:
encrypted
scope:
per-user
tokens:
auto-refreshed
3
Resolve
When your agent calls a
Apify MCP
tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs
speed:
~40ms
check:
before every call
seen by:
nobody
4
Audit
Every
Apify MCP
tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it
history:
90 days
export:
SIEM-ready
logged:
every call
Test other agents
See the same per-user auth pattern across other connectors.
No items found.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
Other connector libraries treat auth as a demo afterthought.
01.
Shared tokens break per-user analytics
A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
One connector today. Ten tomorrow.
“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”
Venu Madhav Kattagoni
Head of Engineering / Von
FAQs
Frequently Asked Questions

Does the agent access Apify as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Apify bearer token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Apify?
Yes. Pass a tool name filter to listScopedTools so the AI agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Apify.

What happens when a user revokes Apify access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Whose Apify account do Actor runs bill against?
The authorizing user's. Actor calls, run aborts, and dataset reads use that user's Apify plan, memory limits, and storage, so scraping spend attributes to the person who triggered it.

Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""apifymcp"": {
""url"": ""https://mcp.scalekit.com/apifymcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.apifymcp]
url = ""https://mcp.scalekit.com/apifymcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""apifymcp"": {
""url"": ""https://mcp.scalekit.com/apifymcp"",
""type"": ""http""
}
}
}