| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 16 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Detects objects in images using YOLO (You Only Look Once) models running locally via the FastMCP server, returning structured bounding box coordinates, class labels, and confidence scores without sending image data to external APIs. The system manages model lifecycle through a post-installation script that automatically downloads YOLO weights and caches them in the models/ directory, enabling offline operation after initial setup.
Unique: Runs YOLO inference locally within the MCP server process rather than calling cloud vision APIs, with automatic model provisioning via post_install.py that downloads and caches weights, enabling AI assistants to perform object detection without external API calls or data transmission
vs alternatives: Faster than cloud-based vision APIs (no network latency) and more private than Google Vision or AWS Rekognition, but requires local GPU/CPU resources and manual model management vs fully managed cloud services
Performs zero-shot image classification and semantic search using CLIP (Contrastive Language-Image Pre-training) models that encode both images and text into a shared embedding space, enabling AI assistants to classify images against arbitrary text labels without retraining. The system uses cosine similarity between image and text embeddings to rank matches, with model weights automatically downloaded via download_clip.py during setup.
Unique: Integrates CLIP embeddings directly into the MCP server with automatic model provisioning, allowing AI assistants to perform semantic image classification against arbitrary text labels without external API calls, using cosine similarity in a shared embedding space
vs alternatives: More flexible than fixed-class models (supports any text label) and more private than cloud APIs, but slower than traditional CNNs and requires more memory than lightweight classifiers
Composites multiple images together using alpha blending and layer operations through OpenCV's addWeighted and bitwise operations, enabling AI assistants to combine images, apply watermarks, or create composite visualizations. The capability supports configurable opacity, blending modes, and positioning of overlay images.
Unique: Implements multi-layer image composition with alpha blending directly in the MCP server through OpenCV, enabling AI assistants to create composite images and apply overlays without external image editing services, with configurable opacity and positioning
vs alternatives: Faster than cloud APIs for simple overlays, integrates with local image processing pipeline, but less sophisticated than full compositing engines in Photoshop or After Effects
Draws text, rectangles, circles, lines, and arrows on images using OpenCV's drawing functions (putText, rectangle, circle, line, arrowedLine), enabling AI assistants to annotate detection results, create visualizations, or mark regions of interest. The capability supports configurable colors, line widths, and font properties for flexible annotation styling.
Unique: Provides comprehensive drawing capabilities (text, rectangles, circles, lines, arrows) directly in the MCP server through OpenCV, enabling AI assistants to annotate images and visualize results without external image editing services, with configurable styling
vs alternatives: Faster than cloud APIs for simple annotations, integrates seamlessly with local detection tools for visualization, but less feature-rich than full annotation tools like Labelbox or CVAT
Exposes image processing operations as MCP tools with standardized schema-based parameter validation, enabling AI clients (Claude, Cursor, Cline) to discover, invoke, and chain image processing operations through the Model Control Protocol. The FastMCP framework handles tool registration, parameter marshaling, and error handling through a middleware stack that validates inputs against JSON schemas.
Unique: Implements the Model Control Protocol (MCP) as the primary interface for tool invocation, with FastMCP framework handling schema validation and middleware orchestration, enabling AI assistants to discover and invoke image processing tools with standardized parameter handling
vs alternatives: Standardized MCP interface enables compatibility with multiple AI clients vs proprietary APIs, but requires MCP client support and adds protocol overhead vs direct function calls
Automatically downloads, caches, and manages computer vision model weights (YOLO, CLIP, EasyOCR) through post-installation scripts (post_install.py, download_models.py, download_clip.py) that provision models into a models/ directory, enabling zero-configuration operation after setup. The system tracks model metadata and provides resource listings through the models://list resource.
Unique: Implements automatic model provisioning through post-installation scripts that download and cache YOLO, CLIP, and EasyOCR models, with metadata tracking through the models://list resource, enabling zero-configuration operation after pip installation
vs alternatives: Fully automated setup vs manual model download and configuration, but requires large initial downloads and disk space vs cloud-based models that require only API keys
Defines multi-step image processing workflows (e.g., remove-background) as MCP prompts that orchestrate multiple tools in sequence, enabling AI assistants to execute complex operations through natural language instructions that are expanded into tool invocation chains. The system uses prompt templates to guide AI reasoning and tool selection.
Unique: Implements complex image processing workflows as MCP prompts that guide AI assistants through multi-step tool invocation chains, enabling natural language orchestration of operations like background removal without explicit step-by-step instructions
vs alternatives: Enables high-level natural language control of complex workflows vs explicit tool chaining, but depends on AI model reasoning and may be less reliable than deterministic pipelines
Provides a configuration system (config.py) that manages runtime parameters for image processing operations, model selection, and server behavior through environment variables and configuration files. The system exposes a config tool through MCP that allows AI assistants to query and modify settings at runtime without restarting the server.
Unique: Exposes configuration management through an MCP tool that allows runtime parameter adjustment without server restart, enabling AI assistants to tune image processing parameters based on specific use cases or image characteristics
vs alternatives: Enables runtime configuration changes vs static configuration files, but lacks validation and persistence mechanisms found in full configuration management systems
+8 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 45/100 vs ImageSorcery MCP 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