Supabase ranks higher at 42/100 vs Weather & Stock Data Server at 31/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Weather & Stock Data Server | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 31/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability integrates multiple financial APIs to fetch real-time stock prices, utilizing a microservices architecture that allows for seamless API orchestration. It employs a caching mechanism to reduce latency and improve response times, ensuring that users receive the most current data without unnecessary delays. The system is designed to handle multiple simultaneous requests efficiently, making it suitable for high-frequency trading applications.
Unique: Utilizes a microservices architecture that allows for dynamic scaling and efficient API orchestration, unlike monolithic systems.
vs alternatives: More responsive than traditional data feeds due to its caching and microservices approach.
This capability allows users to access and analyze historical stock data by querying a dedicated database that stores time-series data. It employs advanced indexing techniques for quick retrieval and supports various analytical functions, such as moving averages and volatility calculations. The integration with data visualization tools enables users to create insightful charts and graphs directly from the retrieved data.
Unique: Employs advanced indexing and analytical functions tailored for financial data, providing faster insights than generic data analysis tools.
vs alternatives: Offers more specialized financial analytics capabilities compared to general-purpose data analysis platforms.
This capability integrates with weather APIs to provide real-time alerts and forecasts, using a subscription model to push notifications to users based on predefined criteria. It employs event-driven architecture to trigger alerts when specific weather conditions are met, ensuring timely updates. Users can customize alert parameters, such as location and severity, to tailor the information to their needs.
Unique: Utilizes an event-driven architecture for real-time alerting, which is more responsive than traditional polling methods.
vs alternatives: Provides faster and more customizable alerting compared to standard weather APIs that only offer static data.
This capability aggregates stock-related news from multiple sources using a combination of web scraping and API integrations. It employs natural language processing (NLP) techniques to filter and categorize news articles based on relevance and sentiment, providing users with a curated feed of important updates. The system is designed to update in real-time, ensuring users have access to the latest information.
Unique: Combines web scraping with NLP for real-time sentiment analysis, providing a more nuanced understanding of market sentiment than traditional news feeds.
vs alternatives: Delivers a more comprehensive and sentiment-aware news feed compared to standard financial news aggregators.
This capability allows seamless integration with multiple APIs for both stock and weather data, utilizing a unified interface that abstracts the complexities of individual API calls. It employs a modular design that enables easy addition or removal of APIs, facilitating rapid adaptation to changing data sources. This approach ensures that users can access a wide range of data without needing to manage multiple integrations manually.
Unique: Utilizes a modular design that allows for dynamic API management, making it easier to adapt to new data sources than rigid integration frameworks.
vs alternatives: More flexible than traditional API integration tools that require extensive configuration for each new data source.
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 Weather & Stock Data Server at 31/100.
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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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