| 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 and invoke functions based on a schema that abstracts the underlying implementations. It utilizes a registry pattern to manage multiple function providers, enabling seamless integration with various APIs and services. The server can dynamically route calls to the appropriate provider based on the defined schema, which enhances flexibility and reduces the need for hardcoding specific API calls.
Unique: Utilizes a dynamic function registry that allows for runtime resolution of API calls based on user-defined schemas, rather than static mappings.
vs alternatives: More flexible than traditional API wrappers as it allows for dynamic function resolution at runtime.
This capability provides a framework for managing context across multiple steps in a workflow, allowing for the preservation of state information between function calls. It employs a context object that is passed along through the workflow, ensuring that each function can access and modify shared data as needed. This approach enhances the ability to build complex workflows that depend on previous outputs.
Unique: Implements a context object that flows through the workflow, allowing for dynamic state management without external storage dependencies.
vs alternatives: More efficient than traditional state management solutions as it avoids external database calls for context retrieval.
This capability enables the orchestration of multiple APIs in real-time, allowing for the simultaneous execution of requests and aggregation of responses. It leverages asynchronous programming patterns to handle multiple API calls concurrently, significantly reducing the overall execution time for workflows that depend on multiple external services. The server can also handle error management and retries for failed requests.
Unique: Utilizes an asynchronous event-driven architecture to manage multiple API calls concurrently, optimizing for speed and efficiency.
vs alternatives: Faster than synchronous API calling frameworks as it allows for concurrent execution and response aggregation.
This capability provides a robust mechanism for handling errors that occur during API calls, including automatic retries based on configurable parameters. It uses a strategy pattern to define different error handling behaviors, allowing developers to customize how their applications respond to various failure scenarios. This ensures higher reliability and resilience in workflows that depend on external services.
Unique: Employs a strategy pattern for defining error handling behaviors, allowing for customizable and dynamic error management across workflows.
vs alternatives: More customizable than standard error handling libraries, enabling tailored responses to specific error conditions.
This capability simplifies the management of authentication credentials across multiple API providers by centralizing the storage and retrieval of tokens. It uses a secure vault pattern to store sensitive information and provides an interface for developers to easily manage and rotate credentials without modifying application code. This enhances security and reduces the risk of credential leaks.
Unique: Centralizes authentication management using a secure vault pattern, allowing for easy credential rotation and enhanced security.
vs alternatives: More secure than hardcoded credentials in code, reducing the risk of exposure and simplifying credential management.
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 mcp-server at 24/100. mcp-server 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