| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to trace and audit blockchain transactions by providing detailed insights into transfers, internal calls, logs, and traces. It utilizes a comprehensive indexing mechanism that aggregates data from multiple blockchain nodes, enabling users to visualize the entire transaction flow and understand fund movements. This approach ensures that users can access a complete history of transactions, making it easier to identify patterns and anomalies.
Unique: Utilizes a multi-node data aggregation strategy to provide real-time insights into transaction flows, unlike alternatives that may rely on single-node data.
vs alternatives: More comprehensive than standard explorers by providing detailed internal call data and transaction traces.
This capability enables users to monitor blockchain wallets for balance changes, transaction history, and holder statistics. It employs a real-time event subscription model to track wallet activity and updates, ensuring that users receive timely notifications about significant changes. Additionally, it provides analytical tools to visualize trends and patterns in wallet activity over time, helping users make informed decisions.
Unique: Incorporates real-time event subscriptions for immediate updates on wallet activity, unlike traditional monitoring tools that rely on periodic polling.
vs alternatives: Offers faster insights than conventional blockchain explorers by providing real-time notifications and analytics.
This capability analyzes NFT market trends by aggregating data on sales, ownership changes, and market volume. It uses a combination of data scraping and API integrations to gather information from various NFT marketplaces, allowing users to visualize trends through charts and statistics. This approach provides a holistic view of the NFT market, enabling users to identify emerging trends and make data-driven decisions.
Unique: Combines data from multiple NFT marketplaces for a comprehensive view of market trends, unlike single-source analysis tools.
vs alternatives: Provides a broader perspective on NFT trends compared to tools that focus on individual marketplaces.
This capability visualizes token transfers between wallets, providing an intuitive graphical representation of how tokens move across the blockchain. It leverages graph-based algorithms to map relationships between wallets and transactions, allowing users to see connections and patterns in token distribution. This visualization aids in understanding the flow of tokens and identifying potential market movers.
Unique: Employs advanced graph algorithms to create dynamic visualizations of token movements, unlike simpler tools that provide static data.
vs alternatives: Delivers more insightful visualizations than basic token explorers by highlighting relationships and patterns.
This capability aggregates data from various blockchain sources, including addresses, tokens, blocks, and transactions, into a unified interface. It employs a modular architecture that allows for easy integration of new data sources and APIs, ensuring that users have access to the most relevant and up-to-date information. This flexibility makes it suitable for a wide range of blockchain applications and analyses.
Unique: Features a modular architecture that allows for seamless integration of diverse blockchain data sources, unlike rigid systems that limit extensibility.
vs alternatives: More adaptable than traditional blockchain data tools that require extensive reconfiguration for new sources.
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 DuckChain at 30/100.
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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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