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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Downloads YouTube video subtitles by spawning yt-dlp as a subprocess via spawn-rx, capturing VTT-formatted subtitle files from any public YouTube video URL. The implementation wraps the external yt-dlp binary with reactive stream handling, enabling asynchronous subtitle retrieval without blocking the MCP server. Subtitles are fetched in their raw VTT format before post-processing.
Unique: Uses spawn-rx for reactive subprocess management of yt-dlp rather than direct child_process calls, enabling non-blocking async subtitle downloads integrated into the MCP event loop. This approach avoids blocking the stdio transport that communicates with Claude.
vs alternatives: More reliable than YouTube Data API (no quota limits, no API key required) but slower than direct API calls; trades latency for robustness and cost-free operation.
Parses raw VTT (WebVTT) subtitle files to remove timestamps, cue identifiers, and formatting metadata, extracting clean readable text for LLM consumption. The processor handles VTT-specific syntax (WEBVTT header, timestamp ranges like '00:00:05.000 --> 00:00:10.000', style blocks) and outputs plain text with line breaks preserved for readability. This enables Claude to work with human-readable transcripts rather than machine-formatted subtitle data.
Unique: Implements VTT-specific parsing logic that strips timing metadata and cue identifiers while preserving dialogue flow, specifically optimized for LLM consumption rather than video playback synchronization. The implementation is lightweight and synchronous, avoiding external dependencies.
vs alternatives: Simpler and faster than full subtitle library solutions (like subtitle.js) because it's purpose-built for LLM text extraction rather than general-purpose subtitle handling.
Implements a Model Context Protocol server using StdioServerTransport that communicates with Claude.ai via standard input/output streams. The server exposes YouTube subtitle tools as MCP resources/tools, allowing Claude to invoke subtitle downloading as a native capability. This integration enables seamless tool calling where Claude can request subtitles without explicit API management by the user.
Unique: Uses StdioServerTransport for bidirectional communication with Claude via stdin/stdout, avoiding network overhead and authentication complexity. The server is stateless and designed to be spawned as a subprocess by Claude's MCP client, making it trivial to install and manage.
vs alternatives: Simpler deployment than REST API servers (no port management, no CORS, no authentication) but limited to Claude.ai ecosystem; tightly coupled to MCP protocol rather than being framework-agnostic.
Validates YouTube URLs and detects whether a video has available subtitles before attempting download, preventing wasted subprocess calls to yt-dlp on videos without captions. The implementation leverages yt-dlp's metadata extraction to check subtitle availability without downloading the full subtitle file, enabling fast pre-flight validation. This reduces latency and improves user experience by failing fast on unsupported videos.
Unique: Performs lightweight metadata extraction via yt-dlp without downloading subtitle content, enabling fast availability checks. This two-stage approach (validate → download) prevents wasted processing on unsupported videos while keeping the architecture simple.
vs alternatives: More reliable than regex-based URL validation because it actually queries YouTube metadata, but slower than simple pattern matching; trades latency for accuracy.
Detects available subtitle languages for a YouTube video and allows selection of specific language tracks for download. The implementation queries yt-dlp's language metadata to present options to Claude, enabling multi-language video analysis. When a language is specified, yt-dlp downloads the corresponding subtitle track, supporting both manually-uploaded and auto-generated captions in different languages.
Unique: Leverages yt-dlp's built-in language detection to enumerate available subtitle tracks without downloading them, then allows selective download of specific language variants. This enables efficient multi-language workflows without redundant downloads.
vs alternatives: More flexible than single-language subtitle extraction but requires explicit language specification; no automatic language preference inference like some commercial video APIs.
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 YouTube at 20/100.
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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
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