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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the ModelContextProtocol (MCP) server specification to expose Ref tools as standardized resources accessible to MCP-compatible clients (Claude, LLMs, agents). Uses MCP's resource discovery and tool registry patterns to advertise available Ref operations, handle client requests through the MCP transport layer, and serialize/deserialize tool inputs and outputs according to MCP schema specifications.
Unique: Provides standardized MCP server wrapper specifically for Ref tools, enabling seamless integration into MCP ecosystems without requiring custom protocol adapters or client-side tool bindings
vs alternatives: Enables Ref tools to work natively with Claude and other MCP clients out-of-the-box, whereas direct Ref library usage requires custom integration code for each client platform
Exposes available Ref tools and their schemas through MCP's resource discovery mechanism, allowing clients to query what operations are available, their input parameters, output formats, and usage constraints. Implements MCP's tools list endpoint and schema introspection to provide clients with structured metadata about each Ref tool without requiring hardcoded knowledge of the tool catalog.
Unique: Leverages MCP's standardized schema advertisement pattern to make Ref tool capabilities queryable and self-documenting, eliminating the need for out-of-band tool documentation or hardcoded client knowledge
vs alternatives: Provides runtime tool discovery comparable to OpenAI's function calling, but through MCP's open protocol rather than proprietary APIs, enabling multi-client compatibility
Handles MCP tool call requests by unmarshaling JSON parameters, invoking the corresponding Ref tool with proper argument binding, capturing results or errors, and serializing responses back to MCP format. Implements error handling to catch Ref tool failures and translate them into MCP-compliant error responses, ensuring clients receive structured feedback about tool execution success or failure.
Unique: Implements MCP's tool invocation contract with explicit error handling and parameter marshaling, ensuring Ref tools behave as reliable, composable building blocks in MCP-based agent workflows
vs alternatives: Provides standardized tool invocation semantics across all MCP clients, whereas direct Ref library usage requires each client to implement its own invocation and error handling logic
Manages the underlying MCP transport layer (typically stdio or HTTP), parsing incoming JSON-RPC 2.0 messages, routing them to appropriate handlers (tool discovery, tool invocation, resource access), and sending responses back to clients. Implements MCP's message framing, request/response correlation, and protocol versioning to ensure reliable bidirectional communication between MCP clients and the Ref tools server.
Unique: Implements MCP's transport abstraction layer to decouple Ref tool logic from communication details, allowing the same server to work with multiple client types and transport mechanisms
vs alternatives: Provides standardized protocol handling that works across all MCP clients, whereas custom tool servers require reimplementing JSON-RPC and message routing for each integration
Maintains execution context and state for Ref tools across multiple MCP requests within a single client session, allowing tools to access shared state, previous results, or session-specific configuration. Implements session isolation to ensure that state from one client session does not leak into another, and provides mechanisms for tools to read/write context that persists across multiple invocations within the same session.
Unique: Provides session-scoped state management for Ref tools within MCP's stateless request/response model, enabling multi-step workflows without requiring clients to manage and pass all context explicitly
vs alternatives: Enables stateful tool orchestration within MCP's protocol constraints, whereas stateless approaches require clients to manage all context explicitly or use external state stores
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 @iflow-mcp/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
+1 more capabilities