Supabase ranks higher at 42/100 vs @redocly/mcp-typescript-sdk at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @redocly/mcp-typescript-sdk | Supabase |
|---|---|---|
| Type | Framework | MCP Server |
| UnfragileRank | 23/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides native TypeScript/JavaScript bindings for implementing MCP servers that expose tools, resources, and prompts to LLM clients. Uses a request-response message protocol over stdio, WebSocket, or SSE transports, with automatic serialization/deserialization of MCP protocol messages and type-safe handler registration via decorators or callback functions.
Unique: Official Redocly implementation providing first-class TypeScript support for MCP servers with idiomatic async/await patterns and type-safe handler registration, rather than generic protocol bindings
vs alternatives: More ergonomic than raw JSON-RPC implementations because it abstracts protocol details and provides TypeScript types for all MCP message structures
Automatically generates JSON Schema definitions for tool parameters from TypeScript function signatures or explicit schema objects, enabling LLM clients to understand tool capabilities, required/optional parameters, and type constraints. Supports nested object schemas, enums, arrays, and custom validation rules that are serialized into the MCP tool definition format.
Unique: Integrates TypeScript's type system directly into MCP tool definitions, allowing developers to define tools once and automatically generate both runtime validation and LLM-readable schemas
vs alternatives: More maintainable than manually writing JSON Schema because schema stays synchronized with function signatures through TypeScript's type checker
Provides built-in logging infrastructure that captures MCP protocol messages, handler execution, and errors in structured format. Logs can be directed to console, files, or custom handlers, with configurable verbosity levels. Includes request/response tracing to help developers debug complex interactions between servers and clients.
Unique: Integrates logging directly into the MCP protocol layer, capturing all messages and interactions automatically without requiring developers to add logging code
vs alternatives: More comprehensive than application-level logging because it captures protocol-level details that are invisible to business logic, enabling deeper debugging
Manages the full lifecycle of MCP connections from initialization through graceful shutdown, including resource cleanup, connection state tracking, and error recovery. Provides hooks for custom initialization and cleanup logic, and handles edge cases like client disconnection, timeout, and protocol errors. Ensures resources are properly released even when errors occur.
Unique: Provides explicit lifecycle hooks for connection initialization and cleanup, allowing developers to manage per-client resources without manual state tracking
vs alternatives: More reliable than manual cleanup because it guarantees cleanup runs even when errors occur, preventing resource leaks in long-running servers
Abstracts the underlying transport mechanism for MCP protocol messages, supporting stdio (for local CLI integration), WebSocket (for bidirectional real-time communication), and Server-Sent Events (for unidirectional streaming). Each transport is implemented as a pluggable adapter that handles message framing, connection lifecycle, and error recovery.
Unique: Provides unified transport abstraction layer that allows developers to write transport-agnostic server code and switch between stdio, WebSocket, and SSE at runtime without code changes
vs alternatives: More flexible than single-transport implementations because it supports both local CLI workflows (stdio) and cloud deployments (WebSocket/SSE) from the same codebase
Enables servers to expose named resources (documents, files, knowledge bases) that LLM clients can request by URI. Resources are registered with metadata (name, description, MIME type) and content is served on-demand via a content handler function, supporting text, binary, and streaming content. Clients discover available resources through the MCP protocol and can request specific resource content or list resources matching patterns.
Unique: Integrates resource serving directly into the MCP protocol layer, allowing LLMs to discover and request resources through the same interface as tools, rather than requiring separate API endpoints
vs alternatives: More discoverable than external APIs because resources are enumerable and self-describing through MCP protocol, enabling LLMs to autonomously find relevant content
Allows servers to register reusable prompt templates with variable placeholders that LLM clients can request and instantiate. Templates are stored server-side with metadata (name, description, arguments) and clients can request template completion by providing argument values. The SDK handles variable substitution and returns the completed prompt text, enabling centralized prompt management and versioning.
Unique: Integrates prompt templates into the MCP protocol as first-class objects, allowing LLMs to discover and request prompts dynamically rather than having prompts hardcoded in client applications
vs alternatives: More maintainable than client-side prompt management because prompts are versioned and updated server-side, ensuring all clients use consistent prompt definitions
Implements JSON-RPC 2.0 message routing that maps incoming requests to registered handler functions and automatically serializes responses. Includes built-in error handling with standardized error codes and messages, request ID tracking for correlation, and support for both synchronous and asynchronous handlers. Errors are caught and formatted according to JSON-RPC 2.0 spec with optional stack traces in development mode.
Unique: Provides transparent async/await support for handlers while maintaining JSON-RPC 2.0 compliance, allowing developers to write natural async code without manually managing Promise chains
vs alternatives: More developer-friendly than raw JSON-RPC implementations because it abstracts message routing and error formatting, reducing boilerplate code
+4 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 @redocly/mcp-typescript-sdk at 23/100.
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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