Supadata MCP connector for AI agents

CALL ANY TOOL

3 tools covering scrape, get.

supadatamcp_supadata_check_crawl_status

Check crawl job status and retrieve results. returns status:

Check crawl job status and retrieve results. returns status: scraping, completed, failed, or cancelled.

Parameters

Name

Type

Required

Description

id

string

Required

The job ID returned by supadata_crawl when the crawl job was created.

supadatamcp_supadata_check_extract_status

Check extract job status and retrieve results. returns statu

supadatamcp_supadata_check_transcript_status

Check transcript job status and retrieve results. returns st

supadatamcp_supadata_crawl

Create a crawl job to extract content from all pages on a we

supadatamcp_supadata_extract

Extract structured data from a video url using ai. provide a

supadatamcp_supadata_map

Discover urls on a website

supadatamcp_supadata_metadata

Fetch metadata from a media url (youtube, tiktok, instagram,

supadatamcp_supadata_scrape

Scrape a single web page and return its content. fetches and

supadatamcp_supadata_transcript

Extract transcript from a video or file url. for large files

Build your Agent

Same auth pattern across every framework.

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="supadatamcp")

mcp = MultiServerMCPClient({
    "supadatamcp": {
        "url": "https://mcp.scalekit.com/supadatamcp",
        "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: "supadatamcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/supadatamcp
// 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: "supadatamcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/supadatamcp
// 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="supadatamcp")
# Connect to MCP at https://mcp.scalekit.com/supadatamcp
# Pass: Authorization: Bearer + token

Try these prompts

Paste any prompt into your agent to get started.

Get started

Copy the prompt

Copied

Extract transcript from a video or file URL?

Copy the prompt

Copied

Scrape a single web page and return its content?

Advanced

Copy the prompt

Copied

Fetch metadata from a media URL (YouTube, TikTok, Instagram, Twitter)?

Copy the prompt

Copied

Discover URLs on a website?

SEE HOW AUTH WORKS

User authorises once. Every agent call after uses their token with scope enforcement.

1

Authorize

Your user connects

Supadata 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

Supadata 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

Supadata 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

Supadata 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 Supadata 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 Supadata API key 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 Supadata?
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 Supadata.

What happens when a user revokes Supadata 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 Supadata quota do transcript and crawl jobs use?
The authorizing user's. Crawl, extract, and transcript jobs run against that user's plan, and job status stays queryable only by the user who started it.

Supadata MCP

Supadata MCP tools for AI agents