Supadata MCP | API Key Supadata connector for AI agents

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Get YouTube transcripts, scrape web pages, run searches, retrieve sitemaps, and search YouTube.

supadata_youtube_transcript

Get YouTube transcript

Retrieve the full transcript of a YouTube video by URL or ID.

Parameters

Name

Type

Required

Description

url

string

Required

YouTube video URL or video ID

lang

string

Optional

Language code, e.g. en, es, fr

supadata_web_scrape

Scrape URL

supadata_web_search

Web search

supadata_sitemap

Get sitemap

supadata_youtube_search

Search YouTube

Build your Agent

Drop the toolkit in, point it at the user, and your research agent can use Supadata from the first run.

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import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["supadata"], toolNames: ["supadata_youtube_transcript", "supadata_web_scrape", "supadata_web_search"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

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import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["supadata"], toolNames: ["supadata_youtube_transcript", "supadata_web_scrape", "supadata_web_search"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["supadata"], toolNames: ["supadata_youtube_transcript", "supadata_web_scrape", "supadata_web_search"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

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import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/supadata",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start using Supadata.

YouTube

Copy the prompt

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Get transcript of [YouTube URL].

Copy the prompt

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Summarize the key claims in [video URL].

Copy the prompt

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Search YouTube for [topic].

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Get transcript in [language] for [video].

Web research

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Scrape [URL] and return clean text.

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Search the web for [query] and return top 10 results.

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Get all URLs from [site]'s sitemap.

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Extract pricing information from [URL].

Competitive intelligence

Copy the prompt

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Scrape competitor pricing page at [URL].

Copy the prompt

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Get transcripts from last 3 product demos by [company].

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Search YouTube for [competitor] demo videos.

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Extract all blog posts from [domain] sitemap.

SEE HOW AUTH WORKS

Users authorize Supadata once. Their credentials stay vaulted, every call is checked, and every action is logged.

1

Authorize

Your user connects

Supadata

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

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

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

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

Same per-user auth pattern across other research agents and MCP connectors. Working code, live demos, fork what fits.

SALES

Outbound prospecting agent

Build targeted prospect lists with Apollo, enrich with firmographic data, and draft personalised outreach. Runs on a schedule.

Apollo

View agent

SALES

Sales call prep agent

Pull Granola notes and Attio contact history to draft a pre-call brief before every sales meeting. Zero rep input.

Granola

Attio

View agent

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

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 so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Supadata today. Others 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 research 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.

Are scraped pages cached or shared across tenants?

No cross-tenant caching. Each request uses the authorizing user's API key. Credits and rate limits apply per key. Results are never shared across users in the vault.

Supadata

Tools your research agent reaches for on Supadata, scoped per user.