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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability provides users with the ability to analyze stock and crypto data using dual-timeframe charts, specifically daily and weekly. It leverages a time-series data architecture that allows for efficient retrieval and rendering of historical price data, enabling users to visually compare trends across different timeframes. The implementation utilizes caching strategies to optimize performance and reduce latency when switching between timeframes.
Unique: Utilizes a dual-timeframe rendering engine that allows for seamless switching between daily and weekly views without reloading data.
vs alternatives: More efficient than traditional charting tools due to its dual-timeframe caching mechanism.
This capability integrates over 150 technical indicators from the TA-Lib library, allowing users to apply complex calculations to stock and crypto price data. It uses a modular architecture that enables easy addition of new indicators and supports real-time calculations, ensuring that users receive up-to-date analysis as market conditions change. The implementation is designed to handle large datasets efficiently, leveraging parallel processing where possible.
Unique: Offers a comprehensive integration of TA-Lib indicators with real-time processing capabilities, making it suitable for high-frequency trading.
vs alternatives: More extensive indicator support than many standalone analysis tools, with real-time updates.
This capability allows users to screen stocks based on 57 different filters and 81 fields, enabling highly customized searches. It employs a flexible query engine that can process complex filtering criteria and return results quickly. The architecture is designed to handle large datasets efficiently, utilizing indexing techniques to speed up search operations and improve user experience.
Unique: Features a highly customizable screening engine that allows users to combine multiple filters for precise stock selection.
vs alternatives: More filters and fields than typical stock screening tools, providing deeper insights into stock performance.
This capability calculates various financial ratios for stocks, such as P/E ratio, debt-to-equity, and return on equity. It uses a modular design that allows for easy updates and additions of new ratios as needed. The calculations are performed in real-time, pulling the latest financial data from integrated sources, ensuring accuracy and timeliness in the analysis.
Unique: Real-time calculation of financial ratios using live data feeds, ensuring users have the most current information for decision-making.
vs alternatives: Faster and more accurate than manual calculations or static ratio tables.
This capability provides detailed analysis of index constituents, allowing users to view the components of major indices and their performance metrics. It utilizes a relational database structure to efficiently manage and query index data, enabling users to filter and sort constituents based on various criteria. The implementation supports real-time updates to reflect market changes.
Unique: Provides a comprehensive view of index constituents with real-time performance metrics, allowing for timely investment decisions.
vs alternatives: More detailed and up-to-date than many traditional index analysis tools.
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 Technical Analysis at 33/100.
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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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