| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability enables the server to execute functions defined in a schema, allowing seamless integration with multiple AI model providers like OpenAI and Anthropic. It utilizes a modular architecture that abstracts function definitions and their respective API calls, enabling dynamic routing based on user requests. This design choice allows for flexibility in switching between providers without changing the core logic of the application.
Unique: Utilizes a modular function registry that allows dynamic API routing based on user-defined schemas, unlike static function calls in other MCPs.
vs alternatives: More adaptable than traditional MCPs that require hard-coded API calls, allowing for easier integration of new providers.
This capability allows the server to switch between different AI models based on the context of the user query. It employs a context-aware routing mechanism that analyzes the input and determines the most suitable model to handle the request, optimizing response quality and relevance. This is achieved through a combination of natural language processing and predefined context rules.
Unique: Features an advanced context-aware routing system that dynamically selects models based on input analysis, unlike static model assignments.
vs alternatives: More responsive to user needs than alternatives that rely on fixed model configurations.
This capability orchestrates multiple API calls in real-time, allowing for complex workflows that involve several AI services. It utilizes an event-driven architecture that triggers API calls based on user interactions or system events, ensuring that responses are timely and relevant. This approach is designed to handle asynchronous operations efficiently, reducing wait times for users.
Unique: Implements an event-driven architecture that allows for real-time API orchestration, setting it apart from traditional synchronous API handling.
vs alternatives: More efficient than traditional systems that handle API calls sequentially, improving user experience.
This capability formats responses dynamically based on user preferences or application requirements. It leverages a templating engine that interprets user-defined formatting rules and applies them to the output generated by the AI models. This allows for tailored responses that meet specific user needs, enhancing the overall user experience.
Unique: Utilizes a powerful templating engine for dynamic response formatting, unlike static output formats in other systems.
vs alternatives: More flexible than alternatives that provide fixed output formats, allowing for greater customization.
This capability provides comprehensive logging and monitoring of all API interactions and model responses. It employs a centralized logging system that captures detailed metrics and error reports, enabling developers to track performance and diagnose issues effectively. This is achieved through middleware that intercepts requests and responses, logging relevant data without impacting performance.
Unique: Features a centralized logging system that captures detailed metrics and error reports, unlike fragmented logging in other solutions.
vs alternatives: More comprehensive than alternatives that lack integrated logging and monitoring capabilities.
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 godson_1 at 22/100. godson_1 leads on ecosystem, while Supabase is stronger on quality.
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