| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes flight searches across Kiwi.com's aggregated inventory using structured query parameters (origin, destination, dates, passenger count, cabin class). Implements server-side filtering and ranking logic that queries live airline APIs and metasearch partners, returning paginated results with pricing, duration, stops, and availability status. The MCP protocol wraps these queries as tool calls, allowing AI assistants to invoke searches with natural language interpretation translated to structured parameters.
Unique: Direct integration with Kiwi.com's proprietary flight aggregation engine (which combines 1000+ airlines and metasearch partners) exposed via MCP protocol, enabling AI assistants to access live inventory without building separate API integrations or managing authentication credentials
vs alternatives: Provides broader flight coverage than airline-specific APIs (e.g., United, Delta direct APIs) because Kiwi.com aggregates across all carriers; simpler than building custom metasearch because MCP handles protocol translation and credential management server-side
Converts search results into bookable reservations by accepting passenger details (names, contact info, payment method) and submitting them through Kiwi.com's booking engine. Implements PCI-compliant payment processing (likely delegated to third-party processor) and returns booking confirmation with reference number, itinerary details, and receipt. The MCP server abstracts away payment gateway complexity, presenting a single 'book_flight' tool that handles multi-step checkout flows internally.
Unique: Encapsulates Kiwi.com's full booking workflow (passenger validation, seat selection, ancillary upsells, payment processing) as a single MCP tool call, abstracting away multi-step checkout complexity that would otherwise require the AI assistant to manage state across multiple API calls
vs alternatives: Simpler than integrating Kiwi.com's REST API directly because MCP server handles session management and payment tokenization; more complete than airline-direct booking APIs because Kiwi.com's engine supports mixed-carrier itineraries and dynamic pricing
Retrieves, modifies, and cancels existing bookings using booking reference and passenger details as lookup keys. Implements state queries (fetch_booking) that return current itinerary, seat assignments, and ancillary services, plus mutation operations (modify_booking, cancel_booking) that interact with Kiwi.com's reservation system and potentially trigger airline APIs for seat changes or cancellations. MCP server likely maintains session context to avoid re-authentication for sequential operations on the same booking.
Unique: Provides unified interface for querying and mutating bookings across Kiwi.com's multi-airline inventory, handling the complexity of different airline reservation systems (some use GDS like Amadeus, others have proprietary APIs) behind a single MCP tool
vs alternatives: More comprehensive than airline-specific modification APIs because it works across mixed-carrier bookings; simpler than building custom integrations with each airline's reservation system because Kiwi.com abstracts those differences
Enables AI assistants to set up price-watch rules on flight routes, returning notifications when prices drop below specified thresholds or when new cheaper options appear. Likely implemented via background job scheduling on Kiwi.com's servers that periodically re-queries the specified route and compares against baseline prices, triggering webhook callbacks or email notifications to the MCP client. The MCP tool exposes create_price_alert, list_alerts, and delete_alert operations that manage these monitoring rules.
Unique: Delegates price-monitoring logic to Kiwi.com's backend infrastructure rather than requiring the MCP client to implement polling; uses server-side job scheduling to avoid keeping AI assistant connections open for long-running monitoring tasks
vs alternatives: More efficient than client-side polling (which would require the AI assistant to repeatedly call search_flights) because monitoring runs server-side; more integrated than third-party price-alert services (e.g., Hopper, Google Flights alerts) because alerts are tied directly to Kiwi.com's inventory
Constructs complex multi-leg trips (e.g., NYC → London → Paris → NYC) by chaining individual flight searches and applying optimization logic (minimize total duration, minimize total cost, balance layover times). The MCP server likely exposes a high-level 'plan_trip' tool that accepts a list of waypoints and constraints, then internally decomposes into sequential searches and ranks results by user-specified criteria. May implement dynamic programming or greedy algorithms to find optimal routing across multiple segments.
Unique: Implements server-side trip optimization logic that decomposes multi-city requests into sequential searches and applies ranking/filtering algorithms, allowing AI assistants to request complex itineraries in a single MCP call rather than orchestrating multiple search calls and ranking logic themselves
vs alternatives: More sophisticated than simple sequential searches because it applies global optimization across all legs; more practical than building custom constraint-satisfaction solvers because Kiwi.com's MCP server encapsulates the optimization logic
Interprets free-form natural language travel requests (e.g., 'I want to fly from New York to Paris next summer for 2 weeks') and extracts structured parameters (origin, destination, dates, passenger count) that feed into flight search tools. Likely implemented via prompt engineering or fine-tuned language model on the MCP client side (Claude or other AI assistant), but the MCP server may provide schema definitions and validation hints that guide the parsing. The server may also expose a 'validate_parameters' tool that checks if extracted parameters are valid (e.g., airport codes exist, dates are in future).
Unique: Leverages the AI assistant's (e.g., Claude's) native language understanding to parse travel intent, then validates extracted parameters against Kiwi.com's schema via MCP server, creating a feedback loop where the assistant can refine ambiguous requests
vs alternatives: More flexible than rule-based intent parsers because it uses LLM reasoning; more accurate than regex-based parameter extraction because it understands semantic relationships (e.g., 'next month' relative to current date)
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 https://www.kiwi.com at 22/100. https://www.kiwi.com leads on quality, while Supabase is stronger on ecosystem. Supabase also has a free tier, making it more accessible.
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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