| 0 |
| 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 through a schema-based registry that supports multiple model providers. It utilizes a flexible architecture that can dynamically adapt to different APIs, enabling seamless integration with various AI models. The design choice to implement a schema registry allows for easy addition of new providers without disrupting existing functionality, making it highly extensible.
Unique: The use of a schema registry for function definitions allows for dynamic adaptation to new AI models without code changes.
vs alternatives: More flexible than static function calling libraries as it allows for easy integration of new model providers.
This capability enables the orchestration of API calls with an awareness of the context provided by previous interactions. It leverages a context management system that retains relevant information across multiple calls, allowing for more intelligent and coherent interactions with APIs. This design choice minimizes the need for repetitive data input by users and enhances the overall efficiency of the workflow.
Unique: The context management system is designed to retain information across multiple API calls, enhancing interaction coherence.
vs alternatives: More efficient than traditional API orchestration tools that do not maintain context, leading to less user input.
This capability allows the system to dynamically select the most appropriate AI model based on the user's intent as expressed in their queries. It employs a classification algorithm that analyzes user input and matches it with the strengths of available models, ensuring optimal performance for each request. This approach enhances the user experience by providing tailored responses without requiring users to manually select models.
Unique: Utilizes a classification algorithm to match user intents with model capabilities, enhancing response relevance.
vs alternatives: More responsive than static model selection methods that require user input for model choice.
This capability provides real-time monitoring and logging of all API interactions, allowing developers to track performance metrics and errors as they occur. It integrates with a logging framework that captures detailed information about each request and response, facilitating debugging and performance analysis. This design choice ensures that developers have immediate access to critical data for troubleshooting and optimization.
Unique: Real-time logging is integrated directly into the API interaction layer, providing immediate feedback for developers.
vs alternatives: More immediate than batch logging solutions that require post-processing of logs.
This capability allows the server to handle multiple API requests concurrently using a multi-threaded architecture. It employs a thread pool to manage incoming requests efficiently, ensuring that the server can scale to accommodate high volumes of traffic without degrading performance. This design choice enhances the throughput of the server, making it suitable for applications with demanding performance requirements.
Unique: Utilizes a thread pool for concurrent request handling, significantly improving server throughput under load.
vs alternatives: More efficient than single-threaded architectures that struggle with high concurrency.
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 tedt at 22/100. tedt 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