| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Searches hotel inventory by geographic coordinates, city name, state, or country using a location-aware query engine that translates natural language location inputs into structured search parameters. The MCP server accepts location data through standardized tool schemas and returns filtered property lists with availability status, enabling builders to construct location-first hotel discovery experiences without managing geocoding or inventory APIs directly.
Unique: Implements location-aware search through MCP tool schema that abstracts away geocoding complexity — the server handles coordinate normalization and location disambiguation internally, allowing LLM clients to use natural language location queries without separate geocoding tool calls
vs alternatives: Simpler than building separate geocoding + hotel API integrations because location resolution and inventory search are unified in a single MCP tool, reducing context switching and API call overhead in LLM workflows
Queries real-time hotel availability and pricing data for specific properties, returning structured comparisons of room types, nightly rates, and occupancy status. The capability uses a rate-lookup schema that accepts property IDs and date ranges, returning normalized pricing data across room categories, enabling builders to surface rate variations and occupancy constraints without parsing heterogeneous hotel website formats.
Unique: Normalizes heterogeneous hotel pricing schemas into a unified rate-comparison format through the MCP tool interface, allowing LLM clients to compare room types and rates using consistent field names regardless of the underlying hotel provider's API structure
vs alternatives: More efficient than scraping individual hotel websites because it abstracts rate lookup into a single structured MCP call, reducing latency and eliminating the need for browser automation or HTML parsing in LLM workflows
Retrieves comprehensive property metadata including guest reviews, ratings, promotional offers, and amenity lists for a given hotel. The capability fetches and aggregates review data and active promotions through a property-detail schema, returning structured information that enables builders to display rich property context without making multiple downstream API calls or parsing unstructured review text.
Unique: Aggregates reviews, ratings, and promotions into a single property-detail MCP tool response, eliminating the need for separate review-fetching and promotion-lookup calls — the server handles data enrichment and normalization internally
vs alternatives: Reduces context overhead in LLM workflows compared to calling separate review and promotion APIs because all property metadata is returned in a single structured response, minimizing token consumption and API latency
Generates ready-to-book URLs with embedded parameters for room selection, rate tier, supplements, and optional guest details, allowing builders to create shareable booking links that skip intermediate selection steps. The capability constructs URLs using a booking-link schema that accepts property ID, room type, rate code, and guest information, returning a fully-formed booking URI that preserves all preselections through the checkout flow.
Unique: Encodes all booking parameters (room type, rate, supplements, guest info) into a single URL through the MCP tool, allowing LLM agents to generate complete booking URIs without manually constructing query strings or understanding the hotel provider's URL schema
vs alternatives: Simpler than manually building booking URLs because the MCP server handles parameter encoding and validation, reducing the risk of malformed links and ensuring all preselections are correctly preserved through the booking flow
Exposes hotel search, availability, property detail, and booking link generation capabilities as standardized MCP tool schemas that LLM clients can discover and invoke through native function-calling interfaces. The server implements the Model Context Protocol specification, allowing Claude and other MCP-compatible LLMs to automatically discover available tools, understand their parameters and return types, and execute them with proper error handling and response normalization.
Unique: Implements the Model Context Protocol specification to expose hotel capabilities as discoverable, self-describing tools that LLMs can invoke natively without custom prompt engineering — the server handles schema validation, parameter binding, and response formatting according to MCP standards
vs alternatives: More robust than custom function-calling implementations because it uses a standardized protocol (MCP) that multiple LLM platforms support, reducing vendor lock-in and enabling tool reuse across different LLM clients and frameworks
Fetches comprehensive property information including amenities, room descriptions, photos, star ratings, guest reviews, location details, and contact information for a specific hotel. Aggregates metadata from the hotel database to provide rich context for decision-making, enabling agents to present detailed property profiles without requiring separate web scraping or manual lookups.
Unique: Bundles property metadata (amenities, reviews, photos, contact info) into a single MCP tool call rather than requiring separate API calls for each data type, reducing latency and simplifying agent logic for comprehensive property lookups
vs alternatives: Provides structured review and amenity data from the hotel's authoritative source rather than scraping third-party review sites, ensuring consistency with the booking system and reducing legal/ToS risks
Accepts a list of hotel property IDs and date ranges, returning availability status for all properties in a single request. Implements batch query optimization to reduce round-trip latency compared to sequential per-property checks, enabling efficient comparison shopping across multiple hotels without N+1 API call patterns.
Unique: Implements batch availability queries as a distinct MCP tool, allowing agents to request multiple properties' availability in parallel rather than sequentially, reducing total latency for comparison workflows by 50-80% compared to per-property calls
vs alternatives: Optimizes for comparison shopping use cases by batching requests server-side, whereas generic hotel APIs require client-side parallelization and may rate-limit rapid sequential requests
Filters hotel rates and room types by criteria such as price range, amenities, bed configuration, occupancy capacity, and accessibility features. Implements server-side filtering logic to reduce data transfer and client-side processing, enabling agents to narrow results before presenting options to users or making booking decisions.
Unique: Implements filtering as a server-side operation on the hotel inventory system rather than client-side post-processing, reducing data transfer and enabling use of database indexes for fast filtering across large room catalogs
vs alternatives: Filters directly against the hotel's inventory system rather than requiring agents to fetch all rates and filter locally, reducing bandwidth and enabling complex multi-criteria filters (e.g., price + occupancy + amenities) in a single query
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.
MCP Seeker scores higher at 46/100 vs Supabase at 42/100.
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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