| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the ModelContextProtocol server specification to expose Ref functionality as a standardized MCP resource. The server translates MCP protocol messages (resource requests, tool invocations) into Ref API calls and returns results in MCP-compliant format, enabling any MCP-compatible client (Claude Desktop, custom agents) to interact with Ref without direct SDK integration.
Unique: Provides native MCP server implementation for Ref, enabling direct protocol-level integration rather than requiring wrapper SDKs or custom HTTP adapters
vs alternatives: Offers standardized MCP protocol support for Ref access, making it compatible with any MCP client ecosystem rather than being limited to specific LLM platforms or custom integrations
Exposes Ref objects (documents, references, collections, or other Ref entities) as MCP resources with URI-based addressing and content retrieval. The server implements the MCP resources endpoint to list available Ref resources, resolve resource URIs to Ref API calls, and return resource content in a format consumable by MCP clients, enabling LLMs to discover and access Ref data.
Unique: Implements MCP resources interface specifically for Ref, translating Ref's data model into MCP's resource abstraction with URI-based addressing and lazy content loading
vs alternatives: Enables LLMs to access Ref knowledge through standard MCP resources rather than requiring manual context injection or custom Ref-specific client code
Exposes Ref operations as callable MCP tools with schema-based function definitions. The server registers Ref capabilities (e.g., create reference, search, update) as MCP tools with JSON Schema parameter definitions, allowing MCP clients to discover available operations, validate inputs, and invoke Ref actions with automatic parameter marshaling and error handling.
Unique: Wraps Ref operations as MCP tools with automatic schema generation and parameter validation, enabling LLMs to invoke Ref actions without custom integration code
vs alternatives: Provides standardized MCP tool interface for Ref, making it compatible with any MCP-aware LLM or agent framework rather than requiring Ref-specific client libraries
Manages authentication between MCP clients and Ref API by accepting credentials (API keys, tokens, or OAuth) from the MCP client context and using them to authenticate all downstream Ref API calls. The server acts as an authentication bridge, translating MCP client identity into Ref API credentials without exposing secrets to the LLM or storing them in the server.
Unique: Implements credential bridging at the MCP server level, allowing per-request authentication without storing secrets in the server or exposing them to the LLM
vs alternatives: Provides secure multi-user authentication for Ref access through MCP, avoiding the security risks of hardcoded credentials or credential exposure to LLMs
Translates Ref API errors and responses into MCP-compliant protocol messages, ensuring that Ref operation failures, validation errors, and edge cases are properly formatted as MCP error responses. The server maps Ref HTTP status codes and error payloads to MCP error structures with appropriate error codes and messages, enabling MCP clients to handle Ref failures gracefully.
Unique: Implements systematic error translation from Ref API to MCP protocol, ensuring that all failure modes are properly represented in the MCP error model
vs alternatives: Provides proper error handling and protocol compliance for Ref through MCP, avoiding silent failures or malformed error responses that would break MCP client error handling
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 ref-tools-mcp at 20/100.
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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
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