| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability aggregates and analyzes live sports betting odds from multiple sources using a microservices architecture that allows for rapid data retrieval and processing. It employs WebSocket connections for real-time updates, ensuring that users receive the latest odds and line movements without significant delays. The system is designed to handle high-frequency data streams, making it suitable for dynamic betting environments.
Unique: Utilizes WebSocket connections for live updates rather than traditional polling methods, reducing latency and improving responsiveness.
vs alternatives: More responsive than traditional APIs that rely on polling, providing updates within milliseconds.
This capability generates a visual representation of liquidation events across various cryptocurrencies by processing real-time blockchain data. It employs data aggregation techniques to compile liquidation events and uses a heatmap rendering engine to display this information intuitively. The visualization is updated dynamically, allowing users to see trends and patterns as they develop.
Unique: Combines real-time blockchain data with advanced visualization techniques to provide a unique perspective on market dynamics.
vs alternatives: Offers more interactive and visually appealing insights compared to static reports from competitors.
This capability tracks new token launches across multiple blockchain protocols by integrating with various blockchain explorers and APIs. It employs a polling mechanism to check for new token listings at regular intervals and compiles this information into a centralized dashboard for users. The system is designed to filter and highlight trending tokens based on user-defined criteria.
Unique: Integrates with multiple blockchain protocols to provide a comprehensive view of new token launches, unlike competitors that focus on a single chain.
vs alternatives: More comprehensive than single-chain solutions, offering insights across various blockchain ecosystems.
This capability analyzes social media and news sentiment regarding cryptocurrencies by employing natural language processing (NLP) techniques to parse and interpret text data from multiple sources. It aggregates sentiment scores and trends, providing users with insights into market psychology and potential price movements. The system uses a combination of sentiment analysis algorithms and machine learning models to improve accuracy over time.
Unique: Utilizes advanced NLP techniques tailored for cryptocurrency discussions, enhancing the relevance of sentiment scores compared to generic models.
vs alternatives: More tailored to cryptocurrency markets than general sentiment analysis tools, providing deeper insights.
This capability allows users to integrate data from various blockchain protocols seamlessly, utilizing a modular architecture that supports multiple data sources. It employs a unified API layer that abstracts the complexities of interacting with different protocols, enabling users to fetch and analyze data without needing to understand the underlying differences between blockchains.
Unique: Features a unified API layer that simplifies interactions across diverse blockchain protocols, unlike traditional methods that require separate integrations.
vs alternatives: More efficient than traditional multi-chain solutions that require custom integrations for each blockchain.
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.
BotIndex scores higher at 42/100 vs Supabase at 42/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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
+1 more capabilities