| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability uses real-time on-chain data aggregation to identify and track large cryptocurrency transactions, known as whale movements. By leveraging a combination of Ethereum RPC calls and cross-chain data signals, it provides insights into market trends and potential price movements, enabling users to make informed trading decisions. The architecture supports seamless integration with multiple blockchain networks, enhancing the breadth of data available for analysis.
Unique: Utilizes a multi-chain architecture that integrates data from Ethereum, Bitcoin, and Hyperliquid, allowing for comprehensive tracking across different ecosystems.
vs alternatives: More comprehensive than single-chain trackers by providing cross-chain visibility and insights.
This capability performs deep analysis of wallet addresses to identify ownership patterns, transaction histories, and associated entities. It employs machine learning algorithms to classify wallets based on their behavior and interactions across multiple exchanges and chains. The integration with various blockchain data sources allows for a holistic view of wallet activities, enhancing the profiling accuracy.
Unique: Incorporates advanced ML techniques to enhance wallet profiling accuracy, distinguishing it from traditional heuristic-based methods.
vs alternatives: Provides deeper insights through machine learning compared to basic transaction history analysis tools.
This capability allows users to monitor real-time exchange flows by directly querying exchange APIs through Ethereum RPC. It captures and analyzes trades, liquidity movements, and order book changes, providing users with timely insights into market dynamics. The architecture supports rapid data retrieval and processing, ensuring that users receive up-to-date information to inform their trading strategies.
Unique: Combines direct RPC access with advanced data processing techniques, enabling faster and more reliable exchange flow monitoring than typical REST API methods.
vs alternatives: Faster data retrieval compared to REST-based monitoring tools due to direct blockchain queries.
This capability generates trading signals based on cross-chain data analysis, identifying trends and correlations between different cryptocurrencies. By aggregating data from Ethereum, Bitcoin, and Hyperliquid, it provides users with actionable insights that can inform their trading strategies. The implementation uses a combination of statistical analysis and machine learning to enhance signal accuracy and relevance.
Unique: Utilizes a unique cross-chain data aggregation method that enhances signal generation compared to single-chain analysis tools.
vs alternatives: Provides a broader perspective on market trends by analyzing multiple blockchains simultaneously.
This capability employs machine learning algorithms to predict market trends based on historical blockchain data and transaction patterns. By training models on extensive datasets, it can forecast price movements and market behavior, providing users with predictive insights that can guide their investment decisions. The architecture supports continuous learning, allowing the model to adapt to changing market conditions.
Unique: Incorporates a continuous learning framework that allows for real-time adaptation of models to new market data, enhancing prediction accuracy.
vs alternatives: More adaptive than static prediction models that do not update with new data.
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.
Bitpoort scores higher at 46/100 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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