Supabase ranks higher at 42/100 vs Flight and Stay Search Server at 32/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Flight and Stay Search Server | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 32/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Searches for one-way flights between two airports using the Duffel API, accepting departure/arrival airports, date, passenger count, and cabin class preferences. The MCP server translates user parameters into Duffel API requests, returning structured flight offers with pricing, duration, stops, and airline details. Implements parameter validation and error handling for invalid airport codes or dates.
Unique: Exposes Duffel's flight search as an MCP tool, enabling LLM agents to natively invoke flight searches without custom HTTP client code; uses Duffel's unified API abstraction across 500+ airlines rather than scraping individual airline sites
vs alternatives: Simpler integration than building custom Duffel clients or using REST APIs directly because MCP handles serialization, error handling, and context management automatically
Searches for round-trip flights by accepting outbound and return dates, applying the same multi-criteria filtering (cabin class, passenger count, airlines) to both legs. The server constructs two coordinated Duffel API requests and correlates results, returning paired flight offers with combined pricing and total journey duration. Supports flexible return dates within a date range.
Unique: Automatically correlates outbound and return flight legs into logical round-trip pairings with combined pricing, rather than returning separate one-way results that require client-side matching logic
vs alternatives: Reduces client complexity compared to manually pairing one-way searches; Duffel's native round-trip endpoint provides better pricing optimization than sequential one-way bookings
Constructs multi-leg flight itineraries by chaining multiple flight searches (e.g., NYC→London, London→Paris, Paris→NYC) with user-specified dates for each leg. The server validates routing logic (arrival airport of leg N matches departure of leg N+1), executes sequential Duffel API calls, and aggregates results into a single multi-city itinerary with cumulative pricing and duration. Supports 3+ cities with flexible date ranges per leg.
Unique: Implements client-side multi-city orchestration by chaining one-way searches with routing validation, enabling LLM agents to reason about complex itineraries without requiring Duffel's enterprise multi-city API tier
vs alternatives: More flexible than airline-specific multi-city tools because it aggregates across 500+ airlines via Duffel; cheaper than enterprise multi-city APIs for low-volume use cases
Retrieves detailed information for a specific flight offer by ID, including passenger-level pricing breakdown, baggage allowances, seat availability, cancellation policies, and airline-specific terms. The server calls Duffel's offer detail endpoint and enriches results with parsed policy text and fare rules. Returns comprehensive booking-ready information without requiring a separate booking step.
Unique: Exposes Duffel's offer detail endpoint as an MCP tool with automatic policy parsing and enrichment, enabling LLM agents to explain complex booking terms in natural language without manual policy extraction
vs alternatives: More comprehensive than basic search results because it includes passenger-level pricing, baggage rules, and cancellation policies; avoids requiring separate API calls to fetch offer details
Searches for accommodations (hotels, hostels, vacation rentals) using location, check-in/check-out dates, guest count, and room preferences. The server queries Duffel's accommodation API (or integrated partner like Booking.com), returning structured results with pricing, amenities, guest reviews, ratings, and availability. Supports filtering by price range, star rating, and amenity preferences (WiFi, parking, pool, etc.).
Unique: Integrates accommodation search alongside flight search in a single MCP server, enabling LLM agents to plan complete trips (flights + hotels) without switching between multiple tools or APIs
vs alternatives: Unified interface for flight + accommodation reduces context switching compared to separate flight and hotel APIs; Duffel's aggregation across multiple providers simplifies integration vs. building custom Booking.com/Expedia connectors
Retrieves comprehensive details for a specific accommodation by ID, including full photo gallery, guest review text and ratings, cancellation policies, house rules, and amenity descriptions. The server calls Duffel's accommodation detail endpoint and structures results for easy consumption by LLM agents or UI rendering. Includes aggregated review sentiment analysis if available.
Unique: Structures accommodation details with embedded review text and photo metadata, enabling LLM agents to summarize reviews and describe accommodations in natural language without requiring separate review aggregation
vs alternatives: More detailed than basic search results because it includes full review text and photo galleries; avoids requiring separate API calls to fetch accommodation details
Analyzes multiple flight offers from a search result to identify price trends, fare rules, and value propositions. The server compares base fares, taxes, fees, baggage allowances, and cancellation policies across options, returning a structured comparison with recommendations (cheapest, fastest, best value). Implements basic heuristics for value scoring (price per hour of travel, baggage flexibility, etc.).
Unique: Implements client-side comparison logic within the MCP server, enabling LLM agents to reason about trade-offs (price vs. duration vs. baggage) without requiring separate comparison tools or external analytics
vs alternatives: More integrated than external comparison tools because it operates on Duffel data directly; simpler than building custom scoring models because it uses heuristics rather than ML
Validates all input parameters (airport codes, dates, passenger counts, cabin classes) before executing Duffel API calls, returning structured error messages for invalid inputs. Implements checks for date logic (return date after departure, check-out after check-in), passenger count limits, and supported cabin classes. Catches and translates Duffel API errors (rate limits, invalid airports, unavailable dates) into user-friendly messages.
Unique: Implements pre-flight validation within the MCP server, preventing invalid requests from reaching Duffel's API and reducing quota consumption; translates Duffel API errors into LLM-friendly messages
vs alternatives: More efficient than client-side validation because it reduces wasted API calls; more comprehensive than basic type checking because it validates business logic (date ordering, passenger limits)
+1 more 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.
Supabase scores higher at 42/100 vs Flight and Stay Search Server at 32/100.
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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