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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification, exposing a standardized interface that allows Claude and other MCP-compatible clients to discover and invoke server capabilities through JSON-RPC 2.0 message passing. The server handles protocol negotiation, capability advertisement via the initialize handshake, and request/response routing according to MCP specification, enabling bidirectional communication between AI clients and local/remote tools.
Unique: unknown — insufficient data on specific MCP implementation details, transport layer choices, or custom extensions beyond base spec
vs alternatives: Provides standards-compliant MCP server foundation that integrates with Claude and other MCP clients without requiring custom protocol implementations
Provides a mechanism to register discrete tools/functions with the MCP server and automatically generate JSON Schema descriptions that advertise tool signatures, parameters, and documentation to connected clients. The server maintains a capability registry that clients query during initialization, enabling Claude and other AI clients to discover available tools and understand their input/output contracts without hardcoded knowledge.
Unique: unknown — insufficient data on schema generation approach, whether it uses reflection/introspection, code parsing, or manual definition, and how it handles complex type systems
vs alternatives: Enables dynamic tool discovery through standard JSON Schema, reducing manual integration work compared to systems requiring hardcoded tool definitions on the client side
Routes incoming MCP tool call requests to registered handler functions, managing parameter binding, execution context, and response serialization. The server parses tool invocation messages, validates parameters against registered schemas, executes the corresponding handler with proper error isolation, and returns results or errors back to the client in MCP-compliant format, enabling reliable tool execution with proper error propagation.
Unique: unknown — insufficient data on routing implementation (dispatch table, reflection-based lookup, etc.), concurrency model (async/await, thread pool, etc.), and error isolation strategy
vs alternatives: Provides MCP-standard request routing that integrates seamlessly with Claude's tool calling, eliminating custom protocol parsing compared to building tool servers from scratch
Manages the full lifecycle of MCP client connections including initialization handshake, capability negotiation, session state tracking, and graceful disconnection. The server implements the MCP initialize/initialized protocol sequence, maintains per-client context, handles connection timeouts and unexpected disconnections, and ensures proper resource cleanup when clients disconnect, enabling reliable long-lived connections between clients and the tool server.
Unique: unknown — insufficient data on connection transport (stdio, HTTP, WebSocket), session state storage, timeout/keepalive mechanisms, or multi-client coordination patterns
vs alternatives: Implements MCP protocol lifecycle management, reducing boilerplate compared to building connection handling from raw socket/HTTP libraries
Integrates with the Smithery MCP server marketplace, enabling the server to be discovered, installed, and managed through Smithery's package management system. The server includes metadata (name, description, version, author) and follows Smithery conventions for packaging and distribution, allowing developers to install hady_mcp via Smithery CLI and automatically configure it in their MCP client setup.
Unique: unknown — insufficient data on specific Smithery integration points, metadata format, or custom installation hooks
vs alternatives: Provides Smithery marketplace integration, enabling one-command installation and automatic configuration compared to manual GitHub cloning and setup
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 hady_mcp at 24/100. hady_mcp 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