| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to discover festivals by specifying their location, utilizing a geo-querying mechanism that integrates with various event databases. It employs a combination of RESTful API calls to fetch festival data based on geographic coordinates or city names, ensuring accurate and relevant results based on user input. The system also caches frequent queries to improve response times for popular locations.
Unique: Utilizes a hybrid caching mechanism that reduces API calls for frequently queried locations, enhancing performance.
vs alternatives: More efficient than traditional event search tools due to its caching strategy, which minimizes redundant API requests.
This capability enables users to filter and compare festivals based on specific date ranges. It leverages a date parsing library to interpret user input and applies it to filter results from the festival database. The comparison feature allows users to view key details side-by-side, such as dates, venues, and lineups, facilitating informed decision-making.
Unique: Incorporates a robust date parsing mechanism that allows for natural language date input, enhancing user experience.
vs alternatives: More user-friendly than static comparison tools, allowing for flexible date input and dynamic results.
This capability allows users to filter festivals by genre, utilizing a tagging system that categorizes events based on musical styles, themes, and activities. It integrates with a genre classification model that ensures accurate tagging of festivals, allowing users to find events that match their specific interests. The filtering process is optimized for speed, ensuring quick retrieval of relevant results.
Unique: Employs a dynamic tagging system that allows for real-time updates and user-generated tags, enhancing the relevance of search results.
vs alternatives: More flexible than traditional event listings, allowing for user-specific genre searches that yield tailored results.
This capability retrieves detailed information about festival lineups, including artist names, performance times, and stages. It uses a structured data format to parse and display lineup information, integrating with external APIs that provide real-time updates on artist schedules. The system is designed to handle large datasets efficiently, ensuring quick access to lineup details for multiple festivals.
Unique: Integrates with real-time artist scheduling APIs to provide up-to-date lineup information, enhancing user engagement.
vs alternatives: More timely than static lineup resources, providing users with the latest updates on festival performances.
This capability assists users in planning their festival trips by providing recommendations for accommodations, transportation, and local attractions. It utilizes a recommendation engine that analyzes user preferences and past behavior to suggest optimal travel arrangements. The system also integrates with mapping services to provide directions and travel time estimates, ensuring a comprehensive planning experience.
Unique: Combines user behavior analysis with real-time data from travel services to provide personalized trip planning recommendations.
vs alternatives: More integrated than standalone travel apps, offering tailored recommendations specifically for festival-goers.
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 Festival Finder 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
+1 more capabilities