| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the MCP server specification as a standalone process that manages bidirectional JSON-RPC communication with MCP clients (Claude, IDEs, agents). Handles server initialization, capability advertisement, resource/tool registration, and graceful shutdown through the MCP protocol handshake and message routing layer.
Unique: unknown — insufficient data on specific implementation details (language, framework, architectural patterns used)
vs alternatives: MCP servers provide standardized tool exposure compared to custom REST APIs or webhook-based integrations, enabling seamless Claude integration without client-side routing logic
Registers custom tools with schema-based definitions (name, description, input schema) and routes incoming tool invocation requests from MCP clients to handler functions. Validates input against JSON Schema, executes the appropriate handler, and returns structured results back through the MCP protocol.
Unique: unknown — insufficient data on validation framework, error handling strategy, or async execution patterns
vs alternatives: Schema-based tool definition is more portable than hardcoded function signatures, allowing tools to be discovered and validated by any MCP-compatible client without custom integration code
Registers static or dynamic resources (documents, files, data) with metadata (URI, MIME type, description) and serves their content on-demand to MCP clients. Supports resource listing with filtering and individual resource reads, enabling clients to access application data without direct API calls.
Unique: unknown — insufficient data on resource caching strategy, streaming support, or access control mechanisms
vs alternatives: MCP resource serving provides discoverable, metadata-rich data access compared to raw file serving or API endpoints, enabling Claude to understand what data is available before requesting it
Registers reusable prompt templates with variable placeholders and metadata, allowing MCP clients to discover and invoke templated prompts with argument substitution. Enables standardization of prompt patterns across multiple AI interactions without duplicating prompt logic in client code.
Unique: unknown — insufficient data on template syntax, variable binding mechanism, or prompt versioning approach
vs alternatives: Server-side prompt templates enable consistent prompt management and updates without client redeployment, compared to embedding prompts in client code or external prompt management systems
Provides a sampling interface that allows MCP servers to invoke LLM models (Claude, etc.) through the MCP client's model access. Enables server-side agentic workflows where the server can request LLM completions, reasoning, or tool use orchestration without direct model API access.
Unique: unknown — insufficient data on sampling implementation, model parameter exposure, or agent loop handling
vs alternatives: Server-side sampling through MCP enables agent logic to run on the server without exposing model API keys, compared to client-side agents or direct server-to-model API calls
Manages multiple in-flight tool calls and resource reads using JSON-RPC message IDs for request/response correlation. Implements async/await patterns (Node.js) or asyncio (Python) to handle concurrent operations without blocking. Tracks pending requests, matches responses to requests by ID, and handles timeouts for slow operations. Supports both sequential and parallel tool execution depending on client requirements.
Unique: unknown — likely standard async/await implementation without custom concurrency patterns or optimization.
vs alternatives: MCP's JSON-RPC message ID correlation enables true concurrent request handling, compared to REST APIs that often require sequential polling or WebSocket multiplexing.
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 my-mcp-server at 24/100. my-mcp-server 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
+1 more capabilities