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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows for the encoding of video files into HLS format using a series of media processing robots. It leverages FFmpeg commands orchestrated through the MCP server, enabling seamless integration with AI agents to automate video streaming workflows. The architecture supports dynamic encoding parameters based on user input, making it adaptable to various use cases.
Unique: Utilizes a modular architecture that allows for dynamic adjustment of encoding settings based on real-time user requirements, unlike static encoding solutions.
vs alternatives: More flexible than traditional video encoding services due to its integration with AI agents for automated workflows.
This capability enables the resizing and optimization of images through a series of predefined media processing robots. It employs a pipeline architecture that allows users to specify dimensions and quality settings, which are then processed in real-time, ensuring fast and efficient image handling. The integration with AI assistants allows for automated image adjustments based on contextual needs.
Unique: Features an adaptive resizing algorithm that dynamically adjusts image quality based on user-defined parameters, unlike fixed-size solutions.
vs alternatives: Faster and more efficient than manual resizing tools due to its automated processing pipeline.
This capability allows users to extract text from images using advanced OCR technology integrated within the MCP server. It processes images through a dedicated OCR robot, which analyzes the image content and returns structured text data. The architecture supports multiple languages and custom OCR settings, making it versatile for various applications.
Unique: Incorporates advanced machine learning models for OCR that adapt to different fonts and layouts, enhancing accuracy compared to standard OCR tools.
vs alternatives: More accurate than traditional OCR services due to its use of adaptive learning models.
This capability generates thumbnails from larger images using a streamlined processing pipeline. The MCP server utilizes predefined settings for thumbnail dimensions and quality, allowing for quick generation and integration into web applications. Users can automate thumbnail creation as part of their media processing workflows, enhancing efficiency.
Unique: Utilizes a batch processing approach that allows for simultaneous thumbnail generation from multiple images, improving workflow efficiency.
vs alternatives: Faster than manual thumbnail creation tools due to its automated batch processing capabilities.
This capability allows users to execute custom FFmpeg commands for advanced media processing tasks. The MCP server provides a command interface that integrates with AI agents, enabling users to automate complex media transformations such as format conversions, filters, and effects. This flexibility allows for tailored processing workflows based on specific project needs.
Unique: Offers a direct integration with AI agents, allowing for real-time command execution and feedback, unlike traditional FFmpeg interfaces.
vs alternatives: More user-friendly than command-line FFmpeg due to its integration with AI for automated workflows.
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 Transloadit MCP Server at 40/100.
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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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