| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability enables the execution of functions defined in a schema, allowing for seamless integration with multiple service providers. It uses a model-context-protocol (MCP) architecture to dynamically select and call functions based on the context of the request, ensuring flexibility and extensibility. The schema is defined in a way that abstracts the underlying API details, making it easier for developers to integrate various services without deep knowledge of each API's intricacies.
Unique: Utilizes a dynamic schema-based approach to function calling, allowing for real-time selection of API endpoints based on user context, unlike static function calls in traditional setups.
vs alternatives: More flexible than typical API clients as it allows for dynamic function resolution based on context rather than hardcoded endpoints.
This capability generates responses based on the context provided by the user, leveraging the MCP architecture to maintain state and context across interactions. By storing context information, it can tailor responses to be more relevant and personalized, improving user experience. The implementation uses a combination of session management and context tracking to ensure that the generated responses align with the user's previous interactions.
Unique: Incorporates a robust context management system that allows for real-time updates and retrieval of user context, unlike static context models that do not adapt to ongoing interactions.
vs alternatives: More effective than standard chatbots that lack memory, as it dynamically adjusts responses based on evolving user context.
This capability allows for the transformation of data across different formats, utilizing a set of predefined rules and schemas to convert input data into the desired output format. The MCP framework supports various data types and formats, enabling seamless integration and transformation processes. It employs a modular architecture that allows developers to define custom transformation rules, making it adaptable to various use cases.
Unique: Offers a highly customizable transformation engine that allows developers to define their own transformation rules, unlike rigid transformation tools that only support predefined mappings.
vs alternatives: More flexible than traditional ETL tools, as it allows for on-the-fly transformations based on user-defined rules.
This capability provides real-time monitoring and logging of all interactions and function calls made through the MCP server. It utilizes a centralized logging system that captures detailed information about each request and response, including execution times and error messages. This allows developers to easily track performance metrics and debug issues as they arise, ensuring a smoother operation of the application.
Unique: Incorporates a centralized logging mechanism that captures detailed execution metrics and error information, providing developers with actionable insights in real time, unlike basic logging systems that lack context.
vs alternatives: More comprehensive than standard logging frameworks, as it integrates directly with the MCP to provide context-aware logs.
This capability allows for the orchestration of multiple APIs in a dynamic manner, enabling the execution of complex workflows that involve multiple service calls. It leverages the MCP architecture to manage dependencies and execution order based on the context of the request. Developers can define workflows using a visual interface or code, making it easier to manage and adjust API interactions as needed.
Unique: Utilizes a dynamic orchestration engine that adapts to the context of requests, allowing for real-time adjustments to workflows, unlike static orchestration tools that require predefined sequences.
vs alternatives: More adaptable than traditional API orchestration tools, as it allows for dynamic changes based on user input and context.
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 may-day at 22/100. may-day 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