Supabase ranks higher at 42/100 vs srv-d5200rd6ubrc7390v04g12 at 22/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | srv-d5200rd6ubrc7390v04g12 | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 22/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows for dynamic function calling through a schema-based registry that integrates with multiple model providers. It utilizes a modular architecture to define function signatures and map them to specific API endpoints, enabling seamless orchestration of calls to different models like OpenAI and Anthropic. The design ensures that the server can adapt to various input formats and manage responses effectively, making it versatile for different use cases.
Unique: Utilizes a schema-based registry that allows for dynamic function mapping and integration with multiple AI models, unlike rigid single-provider systems.
vs alternatives: More flexible than traditional API wrappers as it allows for dynamic function registration and multi-provider support.
This capability manages the context state across multiple interactions with AI models, ensuring that relevant information is retained and utilized effectively. It employs a context management system that tracks user inputs and model responses, allowing for a coherent conversation flow. The architecture supports both short-term and long-term context retention, making it suitable for complex interactions.
Unique: Incorporates a dual-layer context management system that supports both transient and persistent context, enhancing interaction quality.
vs alternatives: More robust than simple session-based systems, as it allows for both short-term and long-term context retention.
This capability enables the server to handle multiple requests concurrently through a multi-threaded architecture. By leveraging asynchronous processing and worker threads, it can efficiently manage high volumes of API calls without blocking the main execution thread. This design choice enhances responsiveness and reduces latency for end-users.
Unique: Utilizes a multi-threaded architecture that allows for concurrent processing of requests, significantly improving throughput compared to single-threaded models.
vs alternatives: Outperforms single-threaded systems by handling multiple requests simultaneously, reducing wait times.
This capability provides dynamic routing of API requests to various endpoints based on the request context and parameters. It uses a routing table that can be modified at runtime, allowing developers to add or change endpoints without redeploying the server. This flexibility is crucial for adapting to changing requirements in real-time applications.
Unique: Employs a runtime-modifiable routing table that allows for real-time changes to API integrations, unlike static routing systems.
vs alternatives: More adaptable than traditional static routing systems, allowing for immediate changes without server downtime.
This capability provides comprehensive logging and monitoring of API requests and responses, enabling developers to track usage patterns and performance metrics. It integrates with popular logging frameworks and offers real-time dashboards for monitoring. The architecture supports both local and cloud-based logging solutions, making it versatile for different environments.
Unique: Integrates with multiple logging frameworks and provides real-time dashboards, unlike basic logging systems that lack visualization.
vs alternatives: Offers richer insights than basic logging solutions by providing real-time monitoring and visualization of API usage.
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 srv-d5200rd6ubrc7390v04g12 at 22/100. srv-d5200rd6ubrc7390v04g12 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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