| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability generates detailed code review prompts based on the specific programming language and focus area provided by the user. It utilizes a template-based approach where predefined prompt structures are filled with context-specific information, allowing for a more personalized and relevant review process. The integration with language-specific syntax and semantics ensures that the generated prompts are not only accurate but also actionable for reviewers.
Unique: Utilizes a template-based generation system that adapts to specific programming languages and focuses, enhancing relevance.
vs alternatives: More customizable than generic code review tools, as it tailors prompts to specific languages and contexts.
This capability retrieves the current time in any specified timezone by leveraging a timezone database and converting UTC time to the desired timezone. It employs a simple API call structure that allows users to input their timezone identifier, ensuring accurate and efficient time retrieval across different regions. The implementation is designed to handle daylight saving changes automatically.
Unique: Integrates a comprehensive timezone database that adjusts for daylight saving time changes automatically.
vs alternatives: More accurate than static time retrieval methods, as it dynamically adjusts for timezone changes.
This capability performs quick calculations based on user input by parsing mathematical expressions and executing them using a lightweight evaluation engine. It supports basic arithmetic operations and can handle complex expressions, returning results in real-time. The implementation uses a stack-based evaluation approach to ensure efficiency and accuracy.
Unique: Employs a stack-based evaluation engine for efficient parsing and calculation of mathematical expressions.
vs alternatives: Faster and more flexible than traditional calculators, as it can handle complex expressions in real-time.
This capability allows users to send greetings in multiple languages by utilizing a language translation API to convert a base greeting into the desired language. It supports a variety of languages and can be integrated into messaging applications to enhance user interaction. The implementation includes a fallback mechanism for unsupported languages to ensure a smooth user experience.
Unique: Integrates a robust translation API with a fallback mechanism to enhance user experience across languages.
vs alternatives: More versatile than static greeting templates, as it dynamically translates based on user input.
This capability generates images from text descriptions using a generative model that interprets the input text and creates corresponding visual representations. It employs advanced neural network architectures trained on large datasets to ensure high-quality image outputs. The implementation includes a feedback loop for iterative improvement based on user ratings of generated images.
Unique: Utilizes a generative model with a feedback loop for continuous improvement based on user interactions.
vs alternatives: Produces higher quality images than simpler text-to-image tools by leveraging advanced neural networks.
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 Code Review & Utilities at 28/100.
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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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