| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to define and invoke functions based on a schema that supports multiple model providers. It utilizes a registry pattern to manage function definitions and dynamically resolves calls to the appropriate provider's API, ensuring seamless integration with various LLMs. This architecture enables developers to easily switch between different models without changing the underlying code structure, promoting flexibility and adaptability in model usage.
Unique: The use of a schema-based registry allows for dynamic function resolution, which is not commonly found in other MCP implementations.
vs alternatives: More flexible than traditional API wrappers by allowing dynamic switching between multiple model providers without code changes.
This capability manages the context state across multiple interactions with LLMs, allowing for a coherent conversation flow. It employs a context stack pattern that maintains the history of interactions, enabling the system to provide contextually relevant responses based on previous exchanges. This is particularly useful in applications requiring sustained dialogue or iterative queries with the model.
Unique: Utilizes a context stack to maintain conversation history, which enhances the coherence of responses over time.
vs alternatives: More effective than simple session-based approaches, as it provides a structured way to manage context across multiple interactions.
This capability facilitates the orchestration of API calls to different LLM providers based on user-defined workflows. It employs a microservices architecture that allows for the dynamic composition of API calls, enabling users to create complex workflows that leverage multiple models in a single request. This approach enhances the ability to build sophisticated applications that require the strengths of various models.
Unique: The microservices architecture allows for flexible and dynamic API orchestration, which is not commonly available in simpler integrations.
vs alternatives: More versatile than static API integrations, enabling complex workflows that adapt to user needs.
This capability provides real-time monitoring and logging of all interactions with the LLM APIs, allowing developers to track usage patterns and performance metrics. It uses a centralized logging service that captures API requests and responses, providing insights into the operational aspects of the application. This feature is crucial for debugging and optimizing the performance of AI-driven applications.
Unique: Centralized logging service specifically designed for monitoring LLM interactions, which is often overlooked in other frameworks.
vs alternatives: Provides more detailed insights than standard logging solutions, specifically tailored for AI model interactions.
This capability allows developers to define custom error handling strategies for different types of API responses from LLMs. It employs a strategy pattern that enables users to specify how to handle various error scenarios, such as timeouts or invalid responses, ensuring that applications can gracefully recover from issues. This flexibility is essential for maintaining a smooth user experience in production environments.
Unique: The use of a strategy pattern for error handling provides a level of customization that is often not available in standard API integrations.
vs alternatives: More customizable than traditional error handling approaches, allowing for tailored responses to specific error conditions.
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 mi-20i-mcp at 24/100. mi-20i-mcp leads on ecosystem, while Supabase is stronger on quality.
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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
+1 more capabilities