| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows the MCP server to define and invoke functions based on a schema that supports multiple providers. It utilizes a plugin architecture that enables seamless integration with various external APIs, allowing users to easily switch between different model providers without changing their codebase. The server manages context and state across these calls, ensuring that the right data is passed to the appropriate functions based on the defined schema.
Unique: The artifact's use of a schema-based approach allows for dynamic function invocation, which is adaptable to various API structures.
vs alternatives: More flexible than traditional API wrappers as it allows dynamic switching between multiple model providers.
This capability enables the MCP server to maintain contextual information across multiple requests, allowing for a more coherent interaction with users or applications. It employs a context stack that retains relevant data from previous interactions, which can be referenced in subsequent calls, enhancing the continuity of the conversation or task. This is particularly useful in scenarios where user intent evolves over time.
Unique: Utilizes a context stack mechanism that allows for efficient retrieval and management of state information across requests.
vs alternatives: More efficient than typical session-based approaches as it allows for dynamic context updates without session resets.
This capability allows the MCP server to handle multiple requests concurrently using a multi-threaded architecture. By leveraging Node.js's asynchronous capabilities, it can process incoming requests in parallel, improving response times and throughput. This is particularly beneficial for applications that require high availability and responsiveness under load.
Unique: The server's architecture is designed to maximize Node.js's event-driven model, allowing for efficient handling of concurrent requests.
vs alternatives: Offers better performance under load compared to single-threaded implementations by efficiently utilizing system resources.
This capability allows the MCP server to dynamically generate API endpoints based on the defined schemas and user requirements. It uses a routing mechanism that maps incoming requests to the appropriate handlers based on the schema definitions, enabling rapid prototyping and flexibility in API design. This feature is particularly useful for developers who need to iterate quickly on their API designs without extensive manual coding.
Unique: The use of schema-driven routing allows for automatic endpoint generation, which adapts to changing requirements without manual intervention.
vs alternatives: More flexible than traditional static routing as it allows for rapid changes and adaptations to API structures.
This capability provides real-time logging and monitoring of API requests and responses, allowing developers to track the performance and usage of their MCP server. It employs a logging framework that captures detailed information about each request, including timestamps, response times, and error rates, which can be analyzed for performance tuning and debugging. This feature is essential for maintaining the health and performance of the server.
Unique: Integrates a comprehensive logging framework that captures detailed metrics in real time, enabling proactive performance management.
vs alternatives: Offers more granular insights compared to standard logging solutions by capturing detailed request/response metrics.
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 my-mcp-server-2025 at 24/100. my-mcp-server-2025 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
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