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Every YouTube transcript, web page, and search result your research workflow needs is reachable via Supadata. Supadata MCP gives your agent authenticated access to web data extraction scoped to the user who authorized it.

  • Acts as the user: Access and write actions stay tied to the Supadata account that authorized the agent.
  • Credentials stay vaulted: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: Permissions enforced. 90-day audit trail.
Supadata
agent · Acme Q3
Run
Get the transcript from [competitor's product demo video] and summarize key claims.
S
supadata_youtube_transcript
1.2s
Research agent
Transcript retrieved (47 min). Key claims: sub-100ms latency (claimed 3x), SOC2 Type II certified, 99.99% uptime SLA, supports 40+ integrations. No pricing mentioned. Positioning as enterprise-first.
Sources: YouTube video, 47 min
supadatamcp
1 transcript
18:29
Message Claude...

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

CALL ANY TOOL
Get YouTube transcripts, scrape web pages, run searches, retrieve sitemaps, and search YouTube.
supadata_extract
Extract
Use AI to analyze a video or media URL and extract structured data from it, guided by a natural-language prompt and/or a JSON schema. Returns a jobId : poll Get Extract Results with it until extraction finishes.
Parameters
Name
Type
Required
Description
url
string
Required
URL of the video or media to extract structured data from.
prompt
string
Optional
Natural-language instructions describing what data to extract.
schema
object
Optional
JSON schema describing the exact shape of the structured data to extract.
supadata_web_map
Web map
supadata_account_get
Account get
supadata_web_scrape
Web scrape
supadata_extract_get
Extract get
supadata_youtube_search
Youtube search
supadata_metadata_get
Metadata get
supadata_web_crawl_start
Web crawl start
supadata_transcript_get
Transcript get
supadata_youtube_video_batch
Youtube video batch
supadata_web_crawl_get
Web crawl get
supadata_youtube_channel_videos
Youtube channel videos
supadata_youtube_batch_get
Youtube batch get
supadata_youtube_playlist_videos
Youtube playlist videos
supadata_youtube_video_get
Youtube video get
supadata_youtube_transcript_batch
Youtube transcript batch
supadata_transcript_job_get
Transcript job get
supadata_youtube_transcript_translate
Youtube transcript translate
supadata_youtube_channel_get
Youtube channel get
supadata_youtube_playlist_get
Youtube playlist get
supadata_youtube_transcript_get
Youtube transcript get
Build your Agent
Drop the toolkit in, point it at the user, and your research agent can use Supadata from the first run.
Python · LlamaIndex
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 });
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 }],
});
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
Copied
Get transcript of [YouTube URL].
Copy the prompt
Copied
Summarize the key claims in [video URL].
Copy the prompt
Copied
Search YouTube for [topic].
Copy the prompt
Copied
Get transcript in [language] for [video].
Web research
Copy the prompt
Copied
Scrape [URL] and return clean text.
Copy the prompt
Copied
Search the web for [query] and return top 10 results.
Copy the prompt
Copied
Get all URLs from [site]'s sitemap.
Copy the prompt
Copied
Extract pricing information from [URL].
Competitive intelligence
Copy the prompt
Copied
Scrape competitor pricing page at [URL].
Copy the prompt
Copied
Get transcripts from last 3 product demos by [company].
Copy the prompt
Copied
Search YouTube for [competitor] demo videos.
Copy the prompt
Copied
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.
GTM and RevOps Teams
CRM AI agent
Reads the Granola transcript after every call, extracts next steps and updates the HubSpot record, drafts the follow-up in Gmail, and confirms in Slack, all on the rep's own delegated OAuth.
GTM and RevOps Teams
Sales call prep agent
Reads tomorrow's calls from Google Calendar, mines past Granola notes and Attio history for context, and delivers each rep a prep brief in Slack, scoped to the calls that rep actually owns.
GTM and RevOps Teams
Deal intelligence agent
Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.
GTM and RevOps Teams
Competitive intelligence briefing agent
Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.
Test other agents
Same per-user auth pattern across other research agents and MCP connectors. Working code, live demos, fork what fits.
SALES
Sales call prep agent
Read tomorrow's calls from Google Calendar, mine Granola notes and Attio history, and deliver each rep a prep brief in Slack.
GTM
CRM AI agent
Turn each Granola call transcript into a HubSpot record update, a drafted Gmail follow-up, and a Slack recap.
SALES
Deal intelligence agent
Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.
GTM
Competitive intelligence briefing agent
Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
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.
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"": {
""supadata"": {
""url"": ""https://mcp.scalekit.com/supadata"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.supadata]
url = ""https://mcp.scalekit.com/supadata""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""supadata"": {
""url"": ""https://mcp.scalekit.com/supadata"",
""type"": ""http""
}
}
}