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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 allows users to access real-time stock market data through a RESTful API that integrates with various data sources for live updates. It employs a modular architecture to fetch and aggregate data from multiple endpoints, ensuring users receive the most current information available. The system is designed to handle high-frequency requests efficiently, making it suitable for trading applications.
Unique: Utilizes a lightweight microservice architecture that allows for rapid scaling and efficient data fetching from multiple sources, reducing latency in data delivery.
vs alternatives: More responsive than traditional APIs due to its microservice design, which minimizes bottlenecks during high demand.
This capability enables users to retrieve and analyze historical stock prices over specified time frames. It uses a time-series database to store and query historical data efficiently, allowing users to apply various analytical techniques such as moving averages or volatility calculations. The implementation supports flexible parameterization for date ranges and stock selections.
Unique: Incorporates a time-series database optimized for financial data, enabling efficient querying and analysis of large datasets over time.
vs alternatives: Faster query performance for historical data compared to traditional SQL databases due to its specialized indexing and storage strategies.
This capability allows users to access comprehensive financial statements for publicly traded companies. It integrates with financial data providers to fetch balance sheets, income statements, and cash flow statements. The architecture supports flexible querying, enabling users to specify which financial metrics they need and for which time periods.
Unique: Uses a hybrid approach to aggregate data from multiple financial reporting sources, ensuring comprehensive and up-to-date financial information.
vs alternatives: More comprehensive than single-source financial data providers due to its multi-source aggregation strategy.
This capability aggregates the latest news articles related to specific stocks or the broader market. It employs web scraping and API integrations with news outlets to gather relevant articles, which are then filtered and categorized based on user-defined parameters. The system is designed to provide timely updates and insights into market sentiment.
Unique: Combines web scraping with API data to provide a comprehensive view of stock-related news, ensuring users receive diverse perspectives.
vs alternatives: Offers a broader range of news sources compared to dedicated financial news APIs, enhancing the richness of information.
This capability allows users to construct complex queries with flexible parameters for retrieving stock data, financial statements, and news. It utilizes a query builder interface that supports various filters, such as date ranges, stock symbols, and data types. This design enables users to customize their data retrieval according to their specific analysis needs.
Unique: Features a user-friendly query builder that allows for dynamic parameterization, making it easier for users to tailor their data requests without deep technical knowledge.
vs alternatives: More intuitive than traditional query interfaces, allowing users to build complex queries without needing to write code.
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.
AKShare One MCP Server scores higher at 47/100 vs Supabase at 42/100.
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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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