| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Utilizes Fuse.js for fuzzy searching within a database of over 68,000 pharmaceutical products, allowing for typo tolerance and approximate matching. This capability enables users to find products even with minor spelling errors, enhancing the user experience by reducing the need for exact queries. The implementation leverages a client-side search algorithm that dynamically updates results as the user types, ensuring fast and responsive interactions.
Unique: The use of Fuse.js for fuzzy searching is tailored specifically for the pharmaceutical context, allowing for high accuracy in drug name retrieval.
vs alternatives: More effective than traditional keyword searches in medical databases, as it accommodates user errors and provides relevant results.
Implements a JSON-RPC 2.0 compliant endpoint for Model Context Protocol (MCP), allowing AI agents to interact with the pharmaceutical database through structured requests and responses. This design enables seamless integration with various AI tools and agents, facilitating efficient data retrieval and manipulation. The endpoint is designed to handle multiple concurrent requests while maintaining data integrity and performance.
Unique: The endpoint is specifically designed for the MCP, ensuring compatibility with AI agents while providing structured data access to a comprehensive pharmaceutical database.
vs alternatives: Offers a more standardized and efficient method for AI integration compared to traditional REST APIs, enhancing interoperability.
Features a guided three-step interactive tour that walks users through the process of searching for drugs, viewing details, and understanding the ATC classification. This onboarding process is designed to reduce the learning curve for new users, utilizing a combination of tooltips and live demonstrations to enhance user engagement. The implementation leverages front-end frameworks to create a responsive and intuitive user interface.
Unique: The onboarding process is uniquely structured to guide users through specific tasks relevant to pharmaceutical data, enhancing usability from the start.
vs alternatives: More engaging and effective than static tutorials, as it provides real-time interaction with the system.
Implements a CI workflow that automatically updates the pharmaceutical database on a monthly basis using cron jobs. This ensures that users always have access to the most current drug information without manual intervention. The architecture is designed to pull data from trusted sources, validate it, and integrate updates seamlessly into the existing database structure.
Unique: The use of a CI workflow for automatic updates is specifically tailored for maintaining a pharmaceutical database, ensuring compliance with regulatory standards.
vs alternatives: More reliable than manual updates, as it reduces human error and ensures timely data refreshes.
Incorporates lead capture forms that integrate with Notion CRM and Resend for email notifications, allowing for streamlined user registration and contact management. The forms are designed to capture essential user information while ensuring GDPR compliance through mandatory consent checkboxes. This integration enables the collection of user data for marketing and follow-up purposes while maintaining data privacy standards.
Unique: The integration with Notion CRM and Resend for email notifications is specifically designed to streamline user data management while ensuring compliance with GDPR.
vs alternatives: More efficient than standalone lead capture tools, as it combines data collection with immediate follow-up 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.
sukl-mcp scores higher at 43/100 vs Supabase at 42/100.
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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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