| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to store and manage user-specific facts in a structured knowledge graph format. It utilizes graph database principles to create nodes for entities like people, organizations, and events, and edges to represent relationships between them, enabling complex queries and efficient retrieval of related information. This architecture supports dynamic updates and context-aware recall across conversations, distinguishing it from traditional flat data storage methods.
Unique: Employs a graph-based approach for context storage, allowing for dynamic relationships and efficient querying, unlike traditional relational databases.
vs alternatives: More flexible in managing complex relationships than standard key-value stores, enabling richer context recall.
This capability enables the system to automatically recall relevant user-specific facts during conversations by leveraging the structured knowledge graph. It employs algorithms to prioritize and retrieve the most pertinent information based on the current conversation context, ensuring that responses are personalized and contextually relevant. This is achieved through a combination of semantic search techniques and graph traversal methods.
Unique: Utilizes advanced graph traversal algorithms to retrieve contextually relevant information quickly, enhancing user interaction quality.
vs alternatives: More efficient in maintaining conversational context than linear search methods, reducing response time.
This capability automatically identifies and extracts location data from user interactions, building a hierarchical structure of places. It employs natural language processing (NLP) techniques to parse text for location mentions and uses a predefined taxonomy to categorize these locations, which enhances the knowledge graph's richness and accuracy. The hierarchical structure allows for better contextual understanding of user references to places.
Unique: Combines NLP with a structured approach to build place hierarchies, allowing for richer context than simple keyword extraction.
vs alternatives: More robust in handling complex location references than basic regex-based extraction methods.
This capability allows users to define and manage relationships between different entities within the knowledge graph. It uses a flexible schema that supports various relationship types, enabling users to create complex networks of information that can be queried and analyzed. This feature is particularly useful for applications that require understanding how different entities are interconnected.
Unique: Supports dynamic relationship definitions that can evolve over time, unlike static relationship models in traditional databases.
vs alternatives: More adaptable to changes in entity relationships than rigid relational database schemas.
This capability enables users to perform semantic searches within the knowledge graph, allowing for more intuitive querying of user-specific facts. It employs vector embeddings and similarity search techniques to match user queries with relevant nodes in the graph, enhancing the accuracy and relevance of search results. This approach allows for natural language queries to yield meaningful results based on context rather than exact matches.
Unique: Integrates semantic search capabilities directly into the knowledge graph, allowing for context-aware retrieval that traditional keyword searches lack.
vs alternatives: More effective in understanding user intent than traditional keyword-based search systems.
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 hide-mcp 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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