| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a standalone Model Context Protocol server that accepts client connections via three distinct transport mechanisms: stdio (for local process communication), HTTP (for REST-based polling or long-polling), and WebSocket (for bidirectional real-time communication). The server handles JSON-RPC 2.0 message framing and routing across all transports, allowing a single MCP server instance to serve multiple client types simultaneously without transport-specific business logic.
Unique: Provides unified JSON-RPC routing layer that abstracts transport differences, allowing developers to write transport-agnostic MCP server logic once and expose it via stdio/HTTP/WebSocket without duplication or adapter patterns
vs alternatives: Unlike building separate MCP servers for each transport or using adapter libraries, this unified approach eliminates transport-specific branching logic and ensures consistent message handling across all client types
Manages a pool of active client connections with automatic lifecycle tracking, including connection establishment, heartbeat/keep-alive mechanisms, graceful disconnection, and resource cleanup. The pool maintains metadata about each connection (transport type, client capabilities, session state) and handles reconnection logic for transient failures, preventing resource leaks and zombie connections.
Unique: Implements transport-agnostic connection pooling that works uniformly across stdio, HTTP, and WebSocket clients, with unified heartbeat and reconnection logic rather than transport-specific connection managers
vs alternatives: More lightweight than generic connection pool libraries (like node-pool) because it's MCP-aware and handles protocol-level lifecycle events (initialize, shutdown) rather than just TCP-level connection state
Implements MCP resource protocol methods (list_resources, read_resource) allowing servers to expose files, documents, or data as resources that clients can discover and read. Supports resource metadata (name, description, MIME type), streaming of large resources via chunked responses, and resource filtering/search. Handles resource access control and error cases (not found, permission denied).
Unique: Provides MCP-compliant resource protocol implementation that handles discovery, streaming, and metadata, allowing servers to expose arbitrary data sources as MCP resources without custom protocol handling
vs alternatives: More integrated than generic file serving because it uses MCP resource semantics and integrates with the protocol's discovery and access patterns, whereas HTTP file serving requires separate API design
Implements MCP prompt protocol methods (list_prompts, get_prompt) allowing servers to expose reusable prompt templates that clients can discover and instantiate. Supports prompt metadata (name, description, arguments), argument substitution, and prompt versioning. Enables clients to use server-provided prompts without hardcoding them, facilitating prompt reuse and management.
Unique: Provides MCP-compliant prompt protocol that enables server-side prompt management and discovery, allowing clients to use prompts without hardcoding them and enabling centralized prompt versioning
vs alternatives: More structured than embedding prompts in client code because it uses MCP's prompt discovery and instantiation, enabling prompt reuse across multiple clients and centralized updates
Implements MCP sampling protocol allowing servers to request LLM inference from clients, with model selection, temperature/top-p control, and streaming responses. Servers can ask clients to run inference using their configured LLM (e.g., Claude), enabling tool servers to leverage LLM capabilities without managing their own model. Supports both synchronous and streaming sampling.
Unique: Enables tool servers to request LLM inference from clients via MCP sampling protocol, creating a bidirectional capability where servers can leverage the client's LLM without managing their own models
vs alternatives: More integrated than servers making direct API calls to LLMs because it uses the client's configured model and credentials, enabling seamless integration with the client's LLM setup and cost tracking
Provides a centralized registry for MCP tools with JSON Schema validation, allowing developers to define tools once with input/output schemas and expose them to multiple client types. The registry validates incoming tool calls against declared schemas, enforces type safety, and supports tool discovery via the MCP list_tools protocol, enabling clients to introspect available capabilities before calling them.
Unique: Combines tool registration, schema validation, and MCP protocol compliance in a single registry abstraction, allowing developers to declare tools with schemas once and automatically handle list_tools discovery and call_tool validation without manual protocol handling
vs alternatives: Unlike generic function registries or schema validators, this is MCP-native and integrates directly with the protocol's tool discovery and calling mechanisms, eliminating the need for manual schema-to-protocol translation
Implements complete JSON-RPC 2.0 protocol compliance with automatic message framing, ID tracking, error code mapping, and response correlation. Handles malformed requests, missing required fields, invalid method names, and server errors with proper JSON-RPC error responses (including error codes like -32600 for invalid request, -32601 for method not found). Supports both request-response and notification patterns (requests without IDs that expect no response).
Unique: Provides automatic JSON-RPC 2.0 compliance layer that handles all protocol-level concerns (ID correlation, error codes, notification handling) transparently, so developers only implement business logic without worrying about protocol details
vs alternatives: More complete than ad-hoc JSON-RPC implementations because it handles all edge cases (malformed JSON, missing IDs, invalid methods) with spec-compliant error responses rather than custom error handling
Routes incoming MCP protocol methods (initialize, list_tools, call_tool, list_resources, read_resource, etc.) to appropriate handlers based on method name and request type. Maintains a method registry where developers can register custom handlers for standard MCP methods, with automatic parameter extraction and response formatting. Supports both built-in MCP methods and custom extensions, with fallback to 'method not found' errors for unregistered methods.
Unique: Provides MCP-specific method routing that understands the protocol's method semantics (initialize, call_tool, etc.) and automatically handles parameter extraction and response formatting, rather than generic request routing
vs alternatives: More specialized than generic HTTP routers or RPC dispatchers because it's tailored to MCP's specific method signatures and protocol requirements, reducing boilerplate compared to manual method dispatch
+5 more capabilities
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 @claude-flow/mcp at 30/100.
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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