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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification, exposing tools and resources via the stdio transport mechanism. The server handles JSON-RPC 2.0 message framing over standard input/output, enabling bidirectional communication with MCP clients (Claude Desktop, IDEs, LLM applications). Manages protocol initialization handshakes, capability negotiation, and message routing between client requests and server handlers.
Unique: Provides a reference MCP server implementation using stdio transport, enabling direct integration with Claude Desktop and other MCP clients without requiring HTTP infrastructure or external service hosting
vs alternatives: Simpler deployment than HTTP-based tool servers (no port management, firewall rules, or public endpoints) while maintaining full MCP protocol compliance
Defines a registry of tools with JSON Schema specifications and routes incoming tool invocation requests to corresponding handler functions. The server parses tool call requests from the MCP client, validates arguments against the schema, executes the appropriate handler, and returns structured results. Supports multiple tool definitions with distinct input/output contracts and error handling per tool.
Unique: Implements tool routing via MCP's standardized tool definition format (JSON Schema + handler binding), allowing Claude to discover and invoke tools with full type safety and schema validation before execution
vs alternatives: More robust than ad-hoc function-calling approaches because schema validation prevents invalid invocations, and tool discovery is automatic via MCP protocol rather than requiring manual documentation
Exposes static and dynamic resources (files, documents, API responses, computed data) through the MCP resource interface, allowing clients to read resource content via URI-based requests. Resources are defined with MIME types and optional descriptions, and the server handles content retrieval on demand. Supports both file-based resources (with path resolution) and programmatically-generated resources (computed at request time).
Unique: Implements MCP's resource protocol for on-demand content serving, enabling Claude to fetch files, documents, and computed data directly from the server without embedding everything in the initial context
vs alternatives: More flexible than static context injection because resources are fetched on-demand, reducing initial context size and enabling dynamic content (API responses, database queries) without server restart
Defines reusable prompt templates with variable placeholders and exposes them via the MCP prompts interface, allowing clients to instantiate and execute prompts with custom arguments. The server stores prompt definitions (including instructions, arguments schema, and optional tool bindings) and renders them with provided values at invocation time. Supports multi-step prompts that can chain tool calls and resource access.
Unique: Implements MCP's prompts interface to expose parameterized prompt templates that can bind tools and resources, enabling Claude to execute complex multi-step workflows defined server-side without requiring prompt engineering in each conversation
vs alternatives: More maintainable than embedding prompts in client code because templates are centralized, versioned, and can be updated without client changes; supports tool/resource binding for end-to-end workflow definition
Implements MCP protocol initialization handshake to negotiate supported capabilities between server and client, including tool support, resource serving, prompt templates, and sampling features. The server declares its capabilities during initialization and respects client constraints (e.g., max token limits, supported content types). Enables graceful degradation if client doesn't support certain features.
Unique: Implements MCP's capability negotiation protocol to enable servers and clients to discover each other's features at connection time, allowing dynamic adaptation without hardcoded assumptions about client support
vs alternatives: More robust than assuming client capabilities because negotiation is explicit and standardized, preventing silent failures when clients don't support certain features
Implements comprehensive error handling that returns JSON-RPC 2.0 compliant error responses for various failure scenarios (invalid requests, tool execution errors, resource not found, schema validation failures). The server catches exceptions from tool handlers and resource fetchers, wraps them in standardized error objects with error codes and messages, and returns them to the client without crashing the server process.
Unique: Implements JSON-RPC 2.0 error protocol with MCP-specific error codes, ensuring tool failures and resource errors are communicated back to clients in a standardized format without disconnecting the server
vs alternatives: More reliable than unhandled exceptions because errors are caught and wrapped in protocol-compliant responses, keeping the server alive and allowing clients to handle errors gracefully
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 ruon-ai at 26/100. ruon-ai 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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