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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Creatify AI's video generation capabilities through the Model Context Protocol (MCP) server interface, enabling Claude and other MCP-compatible clients to invoke video creation workflows via standardized tool definitions. The MCP server acts as a bridge layer that translates MCP tool calls into authenticated HTTP requests to the Creatify API, handling request serialization, response parsing, and error propagation back to the client.
Unique: Implements MCP server pattern specifically for Creatify API, enabling seamless integration with Claude and other MCP clients through standardized tool definitions rather than requiring custom API client code
vs alternatives: Provides native MCP integration for Creatify capabilities, whereas direct API usage requires custom HTTP client code and manual context management in agent loops
Generates video content featuring AI-powered avatars by accepting text scripts, voice parameters, and visual customization options, then orchestrating the Creatify backend to render avatar animations synchronized with generated or provided audio. The capability handles avatar selection, pose/gesture configuration, background setup, and temporal synchronization between speech and visual elements.
Unique: Integrates avatar rendering with speech synthesis and temporal synchronization through MCP, allowing agents to specify avatar appearance, script content, and voice characteristics in a single composable tool call
vs alternatives: Simpler than building custom avatar video pipelines; provides end-to-end orchestration from script to rendered video compared to tools requiring separate TTS, animation, and video composition steps
Accepts URLs pointing to web content (articles, blog posts, web pages) and automatically extracts relevant information, generates a video script, and produces a video representation of that content. The capability performs web scraping/content extraction, applies natural language processing to identify key points, generates a narrative script, and orchestrates video rendering with optional avatar or slideshow presentation.
Unique: Combines web content extraction, NLP-based script generation, and video rendering in a single MCP tool, eliminating the need for separate extraction, summarization, and video generation steps
vs alternatives: Automates the entire URL-to-video pipeline within agent workflows, whereas alternatives typically require manual script writing or separate tools for extraction and video generation
Converts text input into natural-sounding speech audio using Creatify's TTS engine, supporting voice selection, accent/language configuration, speech rate adjustment, and emotional tone parameters. The capability handles text normalization, voice model selection, audio rendering, and returns audio files or streams that can be used standalone or integrated into video generation workflows.
Unique: Exposes Creatify's TTS engine through MCP with voice customization parameters, allowing agents to select specific voices and characteristics without managing separate TTS service integrations
vs alternatives: Integrated TTS within the Creatify ecosystem ensures audio-video synchronization and consistent voice selection compared to using external TTS services that require manual sync management
Applies intelligent editing operations to generated or uploaded video content, including scene detection, automatic transitions, color grading, subtitle generation, and content-aware cropping. The capability uses computer vision and AI models to analyze video frames, identify key moments, apply stylistic transformations, and enhance overall production quality without manual frame-by-frame editing.
Unique: Implements AI-driven video analysis and editing through MCP, enabling agents to apply sophisticated post-processing operations (scene detection, color grading, subtitle generation) without requiring external video editing tools or manual intervention
vs alternatives: Automates video post-production within agent workflows, whereas traditional approaches require manual editing software or separate specialized tools for each operation (subtitle generation, color grading, etc.)
Enables processing multiple video generation requests in sequence or parallel, with support for template-based generation, parameter variation, and output organization. The capability handles job queuing, progress tracking, error handling per-item, and aggregated result reporting, allowing agents to generate dozens or hundreds of videos with consistent parameters or systematic variations.
Unique: Provides MCP-based batch orchestration for video generation, allowing agents to specify multiple video jobs with template-based parameter variation and track completion status without managing individual API calls
vs alternatives: Simplifies bulk video generation compared to looping individual API calls; provides job-level abstraction and progress tracking versus managing dozens of separate requests
Generates videos in multiple formats and resolutions optimized for different platforms (YouTube, TikTok, Instagram, LinkedIn) with automatic aspect ratio adjustment, bitrate optimization, and codec selection. The capability handles format conversion, metadata embedding, and platform-specific constraints (duration limits, file size limits, codec requirements) to ensure generated videos are immediately usable on target platforms.
Unique: Automatically handles platform-specific constraints and optimizations (aspect ratio, bitrate, codec, duration) in a single operation, eliminating manual format conversion for multi-platform distribution
vs alternatives: Faster than manual format conversion; ensures platform compliance automatically; handles multiple platforms in single request vs separate tools per platform
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 Creatify at 24/100.
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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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