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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to access real-time subway data by querying the national Tago API, which provides comprehensive information about train schedules, station details, and operational status. It utilizes a microservices architecture to handle multiple requests concurrently, ensuring low latency and high availability for users. The integration with the MCP protocol allows seamless communication between clients and the server, optimizing data retrieval processes.
Unique: The server's architecture is designed for high concurrency, allowing it to handle numerous simultaneous requests efficiently, which is crucial for real-time applications.
vs alternatives: More efficient than traditional REST APIs due to its MCP architecture, which reduces overhead and improves response times.
This capability enables users to perform searches for subway stations using various parameters such as station name or location. It employs a full-text search algorithm optimized for the subway data set, allowing for quick and accurate results. The integration with the MCP protocol allows for efficient querying and retrieval of station information, enhancing user experience in applications that require location-based services.
Unique: Utilizes an optimized full-text search algorithm tailored for subway station data, ensuring rapid and relevant search results.
vs alternatives: Faster and more relevant than generic search APIs due to its focus on subway-specific data.
This capability provides users with access to detailed train timetables, including arrival and departure times for specific stations. It connects to the Tago API to fetch the latest timetable data, ensuring that users receive up-to-date information. The server processes requests and formats the data for easy consumption by client applications, leveraging the MCP protocol for efficient data exchange.
Unique: Fetches and formats timetable data in real-time, ensuring that users have the most current information available at their fingertips.
vs alternatives: More reliable than static timetable databases as it pulls live data directly from the Tago API.
This capability allows users to monitor the operational status of subway lines and stations, providing alerts for delays or service interruptions. It continuously queries the Tago API for updates and processes this information to notify clients of any changes. The use of the MCP protocol enables real-time updates, ensuring that applications can respond promptly to operational changes.
Unique: Provides real-time operational updates by continuously polling the Tago API, ensuring that users receive timely information about service changes.
vs alternatives: More responsive than traditional polling methods due to its MCP architecture, which minimizes latency.
This capability enables seamless integration with MCP clients, allowing them to communicate with the K-Targo Subway Server using the Model Context Protocol. It supports various client types and ensures that data is exchanged efficiently through a well-defined API structure. The server is designed to handle multiple client connections simultaneously, providing a robust solution for developers looking to build interactive applications.
Unique: Designed specifically for MCP client compatibility, ensuring that developers can easily connect and exchange data without extensive configuration.
vs alternatives: More straightforward integration process compared to traditional REST APIs, which often require more complex setup.
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 K-Targo Subway Server 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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