Supadata
MCP ServerFree** - Official MCP server for [Supadata](https://supadata.ai) - YouTube, TikTok, X and Web data for makers.
Capabilities12 decomposed
video transcript extraction with platform-specific parsing
Medium confidenceExtracts full transcripts from YouTube, TikTok, Instagram, and Twitter videos by integrating with the Supadata API, which handles platform-specific authentication, caption retrieval, and text normalization. The MCP server wraps this via the supadata_transcript tool, routing requests through either stdio (local) or Cloudflare Workers (edge) transport layers, with built-in exponential backoff retry logic for rate-limited responses (429 errors).
Directly integrates Supadata's proprietary multi-platform video parsing (YouTube, TikTok, Instagram, Twitter) into MCP protocol, avoiding the need for separate platform-specific SDKs or scraping logic. Supports both local stdio and edge deployment via Cloudflare Workers with unified OAuth 2.0 authentication.
Handles multiple video platforms (YouTube, TikTok, Instagram, Twitter) in a single tool without requiring separate API keys per platform, unlike building individual integrations with each platform's API.
video metadata and structured extraction with ai enrichment
Medium confidenceRetrieves metadata (title, duration, channel info, upload date) and performs AI-powered structured data extraction from video content via supadata_metadata and supadata_extract tools. The extraction uses the Supadata API's LLM-based parsing to convert unstructured video content into schema-compliant JSON, with configurable output schemas passed as tool parameters.
Combines metadata retrieval with LLM-powered schema-based extraction in a single tool, allowing developers to define custom output schemas and have the Supadata API intelligently map video content to those schemas without writing custom parsing logic.
Avoids the need to build separate metadata scrapers and custom LLM prompts for extraction — the Supadata API handles both in a unified, schema-aware manner with built-in retry logic.
github actions ci/cd pipeline with automated testing and deployment
Medium confidenceIncludes GitHub Actions workflows that automate testing, building, and deployment of the Supadata MCP server. The workflows run the test suite (src/index.test.ts), build Docker images, and deploy to container registries or cloud platforms. This enables continuous integration and deployment without manual intervention.
Provides ready-to-use GitHub Actions workflows that automate testing, building, and deployment of the Supadata MCP server, eliminating the need to write custom CI/CD pipelines. Workflows are integrated with the test suite and Docker build process.
Avoids the need to set up custom CI/CD pipelines — the provided GitHub Actions workflows handle testing, building, and deployment automatically on every commit.
smithery mcp registry integration for tool discovery
Medium confidenceIntegrates with the Smithery MCP registry, allowing the Supadata MCP server to be discovered and installed via the Smithery package manager. This enables developers to install Supadata tools via a single command without manually cloning the repository or managing dependencies.
Registers the Supadata MCP server with the Smithery MCP registry, enabling one-command installation via a centralized package manager. Developers can discover and install Supadata tools without manual setup.
Simpler than manual installation or cloning the repository — Smithery provides a centralized registry for MCP server discovery and installation.
single-page web scraping with markdown normalization
Medium confidenceScrapes a single web page and returns content as normalized Markdown via the supadata_scrape tool. The tool handles HTML parsing, content extraction, and Markdown conversion server-side, returning clean, LLM-friendly text without requiring client-side DOM manipulation or HTML parsing libraries. Integrates with the Supadata API's web scraping engine, which abstracts away JavaScript rendering and dynamic content challenges.
Returns Markdown-normalized output optimized for LLM consumption, abstracting away HTML parsing and JavaScript rendering complexity. The server-side processing means clients don't need Puppeteer, Cheerio, or other scraping libraries — just pass a URL.
Simpler than building custom Puppeteer/Cheerio scrapers and returns LLM-friendly Markdown instead of raw HTML, reducing downstream parsing work in agent pipelines.
site-wide url discovery and mapping
Medium confidenceDiscovers all URLs on a website via the supadata_map tool, which crawls the site's structure and returns a list of discoverable URLs. This tool is designed for reconnaissance before batch crawling, allowing developers to understand site topology without fetching full page content. Uses the Supadata API's crawler to follow internal links and build a URL map, respecting robots.txt and site structure.
Provides URL discovery as a separate tool from content scraping, allowing developers to decouple site reconnaissance from data extraction. This enables smarter crawling strategies where agents can decide which URLs to fetch based on the map.
Avoids the need to build custom site crawlers or use generic web crawlers — the Supadata API handles site structure discovery with built-in respect for robots.txt and site conventions.
asynchronous batch web crawling with job polling
Medium confidenceCrawls multiple URLs asynchronously via the supadata_crawl tool, which queues a batch job and returns a job ID. Developers then poll the job status using supadata_check_*_status tools with exponential backoff retry logic. The server manages the async job lifecycle, storing results server-side and returning them when complete. This pattern decouples request submission from result retrieval, enabling high-volume crawling without blocking.
Implements job-based async crawling with built-in polling infrastructure (supadata_check_*_status tools), allowing agents to submit large crawls and check progress without blocking. The server manages job lifecycle and result storage, abstracting away distributed task complexity.
Simpler than building custom job queues or using external task runners — the MCP server handles job submission, polling, and result retrieval with exponential backoff built-in.
job status polling with exponential backoff retry
Medium confidenceProvides supadata_check_*_status tools that poll the status of asynchronous jobs (transcripts, crawls, extractions) with configurable exponential backoff retry logic. The server implements SUPADATA_RETRY_MAX_ATTEMPTS and SUPADATA_RETRY_INITIAL_DELAY configuration variables to control retry behavior, automatically handling transient failures and rate limits (429 errors) without requiring client-side retry logic.
Centralizes retry logic and exponential backoff in the MCP server itself, configured via environment variables (SUPADATA_RETRY_MAX_ATTEMPTS, SUPADATA_RETRY_INITIAL_DELAY), so clients don't need to implement their own retry loops. Handles 429 rate-limit errors transparently.
Eliminates the need for client-side retry logic — the server handles backoff and transient failures automatically, reducing boilerplate in agent code.
mcp protocol transport abstraction with dual deployment modes
Medium confidenceProvides a unified MCP tool interface that works across two transport layers: stdio (local/CLI via src/index.ts) and Cloudflare Workers (edge/serverless via src/worker.ts). The MCP Tool Engine (src/mcp.ts) defines all tools once, and the transport layer abstracts away the underlying communication protocol. Developers can run the same tool definitions locally via npx or deploy to edge infrastructure without code changes.
Implements a clean separation between MCP tool definitions (src/mcp.ts) and transport layers (stdio vs. Cloudflare Workers), allowing the same tool set to be deployed locally or to edge infrastructure without code duplication. Supports both environments with unified configuration.
Avoids the need to maintain separate tool implementations for local and cloud deployments — the MCP abstraction handles transport differences transparently.
oauth 2.0 authentication for edge deployment
Medium confidenceImplements OAuth 2.0 flow for Cloudflare Workers deployment via src/auth-handler.ts and wrangler.toml configuration. Handles user authentication, token exchange, and credential storage for edge-deployed agents. The server manages the OAuth handshake and securely stores credentials in Cloudflare KV storage, enabling multi-user deployments without exposing API keys to clients.
Integrates OAuth 2.0 directly into the Cloudflare Workers entrypoint, allowing multi-user edge deployments without exposing API keys to clients. Credentials are stored in Cloudflare KV, enabling secure, scalable authentication for SaaS applications.
Avoids the need to build custom OAuth flows or manage credentials in application code — the MCP server handles authentication and storage transparently via Cloudflare infrastructure.
environment-based configuration with retry tuning
Medium confidenceProvides centralized configuration via environment variables (SUPADATA_API_KEY, SUPADATA_RETRY_MAX_ATTEMPTS, SUPADATA_RETRY_INITIAL_DELAY) that control API authentication, retry behavior, and backoff strategy. The server loads configuration via dotenv for local deployments and environment variables for cloud deployments, allowing operators to tune retry behavior without code changes.
Centralizes retry and backoff configuration in environment variables, allowing operators to tune resilience without code changes. Supports both local (.env) and cloud (environment variables) deployments with a unified configuration interface.
Simpler than hardcoding retry logic — operators can adjust SUPADATA_RETRY_MAX_ATTEMPTS and SUPADATA_RETRY_INITIAL_DELAY to match their API quota and latency requirements.
docker containerization with multi-stage build
Medium confidenceProvides a multi-stage Dockerfile (node:22-alpine base) that builds the Supadata MCP server in a container, enabling deployment to Docker-compatible environments (Kubernetes, Docker Compose, container registries). The build process compiles TypeScript, installs dependencies, and creates a minimal runtime image optimized for production deployment.
Provides a production-ready multi-stage Dockerfile using node:22-alpine, enabling containerized deployment without requiring developers to write their own Dockerfile. Optimizes for minimal image size and fast builds.
Eliminates the need to write custom Dockerfiles — the provided Dockerfile is optimized for the Supadata MCP server and ready for production deployment.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓AI agents and LLM applications that need to process video content as context
- ✓Developers building research tools that aggregate multi-platform video data
- ✓Teams automating content analysis workflows across YouTube, TikTok, and social media
- ✓Content management systems that need to index and catalog video metadata
- ✓LLM agents performing multi-step reasoning that requires structured video insights
- ✓Data pipelines extracting specific entities or facts from video content
- ✓Teams using GitHub for version control and CI/CD
- ✓DevOps engineers automating deployment pipelines
Known Limitations
- ⚠Requires valid Supadata API key with active quota — no free tier mentioned
- ⚠Transcript availability depends on platform (some videos may lack captions)
- ⚠Asynchronous extraction for long videos requires polling via supadata_check_*_status tools
- ⚠No built-in caching — repeated requests for same video incur API costs
- ⚠Structured extraction quality depends on schema clarity and video content complexity
- ⚠No streaming output — full extraction must complete before returning results
Requirements
Input / Output
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** - Official MCP server for [Supadata](https://supadata.ai) - YouTube, TikTok, X and Web data for makers.
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