| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability utilizes a combination of historical data analysis and machine learning algorithms to predict stock trends in real-time. It integrates with various financial data APIs to gather live market data and applies time-series forecasting models to generate predictions. The architecture supports continuous learning, allowing the model to adapt to new market conditions dynamically.
Unique: Employs a hybrid model combining classical statistical methods with modern machine learning techniques, ensuring robust predictions even in volatile markets.
vs alternatives: More accurate than traditional models due to its adaptive learning mechanism that continuously incorporates new data.
This capability allows users to compare the historical performance of multiple stocks over specified time frames. It leverages a data aggregation layer that pulls historical data from various sources, normalizes it, and presents it in a comparative format. The system uses advanced visualization techniques to help users easily interpret the performance metrics.
Unique: Utilizes a unique data normalization process that allows for accurate comparisons across stocks with different price scales and histories.
vs alternatives: Offers superior visualization options compared to standard data tables, making insights more accessible.
This capability employs machine learning models to evaluate and predict the potential success of stocks based on various financial indicators and market conditions. It integrates a feature selection process that identifies the most relevant indicators for prediction, enhancing the model's accuracy. The system can also suggest stocks based on user-defined criteria.
Unique: Incorporates an advanced feature selection algorithm that dynamically adjusts based on market conditions, improving prediction relevance.
vs alternatives: More tailored recommendations than generic stock screeners due to its predictive modeling approach.
This capability assesses the risk associated with a user's stock portfolio by analyzing the correlation between different stocks and their historical volatility. It uses a Monte Carlo simulation to predict potential future losses and gains, providing users with a comprehensive risk profile. The results are presented in an easy-to-understand format, highlighting areas of concern.
Unique: Utilizes Monte Carlo simulations tailored to individual portfolios, providing a more personalized risk assessment than standard models.
vs alternatives: Delivers deeper insights into portfolio risk compared to traditional risk calculators by simulating various market scenarios.
This capability allows users to set up customizable alerts based on specific stock movements or market conditions. It integrates with real-time data feeds to monitor stock prices and sends notifications through various channels (email, SMS, etc.) when predefined thresholds are met. The system supports complex conditions, enabling users to tailor alerts to their trading strategies.
Unique: Offers a highly customizable alert system that allows for complex conditional logic, unlike simpler alert systems that only trigger on price thresholds.
vs alternatives: More flexible than standard alert systems, enabling tailored notifications that align with specific trading strategies.
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 stock-predictions at 22/100. stock-predictions leads on ecosystem, while Supabase is stronger on quality.
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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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