| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the MCP server specification to expose tools, resources, and prompts to MCP clients via stdio or HTTP transports. Handles bidirectional JSON-RPC 2.0 message routing, connection initialization with capability negotiation, and graceful shutdown. The server manages request/response pairing, error handling, and transport-level concerns while delegating business logic to registered handlers.
Unique: unknown — insufficient data on specific implementation details (language, transport choices, handler architecture)
vs alternatives: Provides standardized MCP compliance vs custom REST/WebSocket APIs, enabling interoperability with any MCP-compatible client without custom integration code
Allows developers to register callable tools with JSON Schema definitions for parameters and return types. The server validates incoming tool calls against schemas, performs type coercion, and routes requests to handler functions. Supports optional argument descriptions, default values, and nested object schemas for complex tool signatures.
Unique: unknown — insufficient data on validation library choice, schema parsing strategy, and error reporting mechanism
vs alternatives: Enforces schema-based validation at the protocol level vs alternatives that defer validation to handler code, catching errors earlier in the request pipeline
Enables registration of resources (files, database records, API endpoints) that clients can request by URI. The server maintains a resource registry with metadata (MIME type, description, update timestamps) and implements content retrieval handlers. Supports optional caching of resource content to reduce repeated computation or network calls.
Unique: unknown — insufficient data on caching strategy, resource discovery mechanism, and URI pattern matching implementation
vs alternatives: Decouples resource content from prompt context via URI references vs embedding everything in context, enabling larger knowledge bases without token overhead
Allows registration of reusable prompt templates with named placeholders that clients can request and complete. The server stores template definitions with descriptions and argument schemas, then performs variable substitution when clients request completions. Supports optional prompt caching to avoid re-parsing identical templates.
Unique: unknown — insufficient data on template syntax, variable substitution engine, and caching implementation
vs alternatives: Centralizes prompt management at the server level vs hardcoding prompts in clients, enabling A/B testing and rapid iteration without client updates
Implements JSON-RPC 2.0 message routing with automatic request ID generation and response correlation. The server maintains an in-memory map of pending requests, matches incoming responses to their corresponding requests, and handles timeouts for orphaned requests. Supports both request-response and notification patterns (one-way messages).
Unique: unknown — insufficient data on request tracking data structure, timeout mechanism, and error recovery strategy
vs alternatives: Provides automatic request/response correlation vs manual ID tracking in client code, reducing bugs from mismatched responses in concurrent scenarios
Abstracts the underlying transport layer to support both stdio (for local CLI integration) and HTTP (for remote clients). The server implements transport-specific serialization (newline-delimited JSON for stdio, HTTP request/response bodies for HTTP) and handles connection lifecycle events. Allows seamless switching between transports via configuration.
Unique: unknown — insufficient data on transport abstraction pattern, serialization strategy, and connection pooling for HTTP
vs alternatives: Single codebase supports both local and remote deployment vs separate implementations, reducing maintenance burden and enabling gradual migration
Implements JSON-RPC 2.0 error response format with structured error codes, messages, and optional diagnostic data. The server catches exceptions from tool handlers and resource retrievers, wraps them in standardized error objects, and includes stack traces or context in development mode. Supports custom error codes for domain-specific failures.
Unique: unknown — insufficient data on error code taxonomy, stack trace filtering, and diagnostic context capture
vs alternatives: Structured error responses enable clients to programmatically handle failures vs generic error strings, improving agent resilience and debugging
Implements MCP protocol handshake where server and client exchange capability declarations during initialization. The server advertises supported features (tools, resources, prompts, sampling) and protocol version, then adapts behavior based on client capabilities. Handles version mismatches gracefully with fallback behavior or connection rejection.
Unique: unknown — insufficient data on capability declaration format, version negotiation algorithm, and fallback behavior
vs alternatives: Explicit capability negotiation prevents silent failures from unsupported operations vs clients blindly assuming feature availability
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-server1 at 25/100. mcp-server1 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