| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to define functions using a schema that supports multiple providers, enabling seamless integration with various APIs. It employs a flexible function registry that can dynamically adapt to different API specifications, allowing developers to easily switch between providers without changing the underlying code structure. This approach enhances interoperability and reduces the complexity of managing multiple API integrations.
Unique: Utilizes a schema-based approach that allows for easy adaptation to different API formats, which is less common in traditional API integration methods.
vs alternatives: More flexible than standard API wrappers, as it allows for dynamic switching between providers without code changes.
This capability manages the contextual data necessary for API interactions, ensuring that relevant information is preserved across multiple calls. It implements a context management system that maintains state and context information, allowing for more intelligent and context-aware API interactions. This is particularly useful for applications that require a series of dependent API calls, as it reduces the need for redundant data passing.
Unique: Features an integrated context management system that automatically tracks and manages state across API calls, which is often a manual process in other frameworks.
vs alternatives: More efficient than traditional methods that require manual context passing, reducing the potential for errors.
This capability enables the artifact to dynamically handle and process responses from various APIs based on predefined rules and conditions. It uses a rule-based engine that interprets API responses and applies logic to determine the next steps or actions, allowing for more adaptive and intelligent workflows. This is particularly beneficial for applications that need to react differently based on varying API outputs.
Unique: Incorporates a rule-based engine for dynamic response handling, which is less common in standard API integration frameworks.
vs alternatives: More adaptable than static response handlers, allowing for greater flexibility in application behavior.
This capability allows the artifact to manage multiple contexts simultaneously, enabling concurrent API calls without interference. It employs a context isolation mechanism that ensures each API call operates within its own context, which is essential for applications that require parallel processing of requests. This design choice enhances performance and reliability when dealing with multiple asynchronous operations.
Unique: Features a context isolation mechanism that allows for true parallel processing of API calls, which is not typically found in simpler frameworks.
vs alternatives: More efficient than traditional approaches that struggle with concurrent requests, reducing the risk of data leakage between contexts.
This capability automatically generates documentation for the defined APIs based on the schema and function definitions provided by the user. It uses a documentation generation tool that parses the schema and creates user-friendly documentation, including examples and usage guidelines. This feature streamlines the process of keeping API documentation up-to-date, which is often a manual and error-prone task.
Unique: Utilizes a schema-driven approach to generate documentation automatically, which is more efficient than manual documentation processes.
vs alternatives: Faster and less error-prone than manual documentation efforts, ensuring consistency across updates.
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 smithery-doc at 24/100. smithery-doc 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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