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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Extracts 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).
Unique: 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.
vs alternatives: 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.
Retrieves 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.
Unique: 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.
vs alternatives: 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.
Includes 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.
Unique: 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.
vs alternatives: Avoids the need to set up custom CI/CD pipelines — the provided GitHub Actions workflows handle testing, building, and deployment automatically on every commit.
Integrates 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.
Unique: 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.
vs alternatives: Simpler than manual installation or cloning the repository — Smithery provides a centralized registry for MCP server discovery and installation.
Scrapes 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.
Unique: 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.
vs alternatives: Simpler than building custom Puppeteer/Cheerio scrapers and returns LLM-friendly Markdown instead of raw HTML, reducing downstream parsing work in agent pipelines.
Discovers 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.
Unique: 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.
vs alternatives: 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.
Crawls 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.
Unique: 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.
vs alternatives: 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.
Provides 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.
Unique: 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.
vs alternatives: Eliminates the need for client-side retry logic — the server handles backoff and transient failures automatically, reducing boilerplate in agent code.
+4 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Supadata at 27/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities