| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability generates personalized greetings by utilizing a language model that processes user input and context to create tailored messages. It supports multiple languages, leveraging a language detection module to adapt greetings based on user preferences. The integration with an MCP server allows for seamless communication and context sharing across different components.
Unique: Utilizes a context-aware language model that adapts greetings based on user preferences and occasion, unlike static templates.
vs alternatives: More flexible than traditional greeting generators as it dynamically adjusts content based on user context.
This capability performs quick calculations by parsing user input for mathematical expressions and executing them using a lightweight expression evaluator. It supports basic arithmetic operations and can handle multiple operations in a single input, returning results in real-time. The integration with the MCP framework allows for efficient processing of requests and responses.
Unique: Employs a real-time expression evaluator that can handle multiple calculations in a single input, enhancing user experience.
vs alternatives: Faster than traditional calculators due to its lightweight processing and integration with the MCP framework.
This capability checks the current time based on user-specified timezones by utilizing a timezone database and converting UTC time to the desired local time. It supports various timezone formats and can return results in multiple formats (12-hour or 24-hour). The MCP integration ensures that requests are handled efficiently and accurately.
Unique: Incorporates a comprehensive timezone database for accurate conversions, unlike simpler implementations that may lack depth.
vs alternatives: More accurate than basic time checkers due to its use of a dedicated timezone database.
This capability generates images based on user-defined text prompts by leveraging a generative adversarial network (GAN) or diffusion model. It interprets the textual input to create visual representations, allowing for customization through parameters like style and resolution. The integration with the MCP framework facilitates efficient handling of image generation requests.
Unique: Utilizes advanced generative models that allow for nuanced interpretations of text prompts, unlike simpler keyword-based image generators.
vs alternatives: Produces higher quality and more relevant images compared to basic text-to-image tools due to its sophisticated model architecture.
This capability creates focused prompts for code reviews by analyzing code snippets and generating specific questions or comments that guide reviewers. It uses static analysis techniques to identify key areas of concern and suggest improvements, integrating seamlessly with the MCP framework to facilitate real-time collaboration.
Unique: Employs static analysis to generate contextually relevant review prompts, enhancing the quality of feedback compared to generic comments.
vs alternatives: Provides more insightful and actionable feedback than traditional code review tools that lack automated prompt generation.
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 Greeting & Utilities at 29/100.
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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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