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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability retrieves the current time by integrating with system-level APIs to access the server's clock. It uses a lightweight request-response model to ensure quick access to the time data, allowing users to fetch the current time in various formats (e.g., UTC, local time). This is distinct because it can be extended to support custom time zones based on user preferences.
Unique: Utilizes a direct call to the system clock API for minimal latency in time retrieval.
vs alternatives: Faster than typical HTTP-based time APIs due to direct system access.
This capability generates personalized greetings by analyzing user input and matching it with predefined templates. It leverages a simple NLP model to understand user context and preferences, allowing for dynamic responses based on the time of day or user history. This personalization aspect sets it apart from static greeting systems.
Unique: Incorporates user context to generate dynamic greetings rather than relying on static messages.
vs alternatives: More engaging than traditional static greeting systems due to its dynamic nature.
This capability performs basic arithmetic calculations by parsing user input and executing the operations using JavaScript's built-in math functions. It supports a variety of operations (addition, subtraction, multiplication, division) and can handle simple expressions, making it easy for users to get quick results without needing a separate calculator tool.
Unique: Directly parses and evaluates expressions in real-time, providing instant feedback without external dependencies.
vs alternatives: Faster and more integrated than using external calculation libraries.
This capability allows users to geocode locations by integrating with third-party geolocation APIs. It processes user input to extract location data and sends requests to the geolocation service, returning structured data such as latitude and longitude. This modular approach enables easy updates or changes to the underlying geolocation provider.
Unique: Modular design allows for easy swapping of geolocation providers without changing the core functionality.
vs alternatives: More flexible than hardcoded geolocation solutions, allowing for quick adaptation to different APIs.
This capability checks live weather data by interfacing with weather APIs to fetch real-time weather information based on user-specified locations. It parses the API response to extract relevant weather details and formats them for user-friendly display. This integration allows users to access up-to-date weather information seamlessly within their applications.
Unique: Utilizes a streamlined API integration to fetch and display weather data quickly and efficiently.
vs alternatives: More responsive than traditional weather APIs due to optimized data fetching strategies.
This capability allows users to check the system's operational status by querying system metrics and health indicators. It uses a lightweight monitoring framework to assess various system parameters and returns a structured report on system health. This provides users with immediate insights into the server's performance and reliability.
Unique: Employs a lightweight monitoring framework for real-time system health checks without significant overhead.
vs alternatives: More efficient than traditional monitoring solutions due to its lightweight design.
This capability allows users to request code reviews by submitting code snippets through a structured API endpoint. It leverages a set of predefined review criteria to assess the quality of the code and returns feedback in a structured format. This capability is designed to streamline the code review process, making it faster and more efficient.
Unique: Utilizes a structured API for code review requests, allowing for rapid feedback without manual intervention.
vs alternatives: Faster than traditional code review processes that rely on manual input and longer turnaround times.
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 my-mcp-server-251209 at 30/100.
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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