| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability leverages real-time data from Dutch rail systems to provide users with up-to-date route information, including transfers and platform details. It utilizes a modular architecture that integrates with various APIs to fetch live data, ensuring that users receive the most current travel information. The system is designed to handle multiple data sources simultaneously, allowing for seamless updates and notifications.
Unique: Integrates directly with multiple live data sources to provide real-time updates, rather than relying on static schedules.
vs alternatives: More accurate than static schedule apps because it updates in real-time based on live data feeds.
This capability monitors live disruptions and provides users with timely alerts and travel advice based on current conditions. It employs a notification system that triggers alerts when disruptions are detected, using a combination of push notifications and in-app messages. The system is designed to analyze disruption patterns and suggest alternative routes or travel adjustments.
Unique: Utilizes a proactive notification system that not only alerts users but also suggests actionable travel adjustments.
vs alternatives: More comprehensive than basic alert systems because it provides actionable advice rather than just notifications.
This capability allows users to check the availability of OV-fiets (public bike rental) at specific stations. It integrates with the OV-fiets API to provide real-time availability data, ensuring users can plan their journeys with accurate information on bike rentals. The system is designed to handle high traffic and provide quick responses to user queries.
Unique: Directly queries the OV-fiets API for real-time availability, providing users with the latest data rather than relying on cached information.
vs alternatives: More reliable than generic bike rental apps because it connects directly to the OV-fiets system for live updates.
This capability tracks live departures and arrivals for Dutch trains, providing users with up-to-the-minute information on train statuses. It employs a polling mechanism to regularly fetch data from the rail network's API, ensuring that users are informed of any changes in real-time. The implementation is designed to handle multiple queries simultaneously, offering a responsive user experience.
Unique: Uses a highly efficient polling mechanism to ensure users receive the latest train status updates without significant delays.
vs alternatives: Faster than many competitors because it minimizes the time between data fetches through optimized polling intervals.
This capability allows users to check ticket prices for various routes and travel options. It integrates with ticketing APIs to fetch current pricing information, enabling users to compare costs and make informed decisions. The system is designed to provide quick responses, even under high load, ensuring that users can access pricing information when needed.
Unique: Directly connects to ticketing APIs to provide up-to-date pricing information, rather than relying on static data or estimates.
vs alternatives: More accurate than static pricing tools because it fetches live data from official ticketing sources.
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 NS Travel at 29/100.
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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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