| 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 detailed sleep metrics by integrating with Ultrahuman's API, which aggregates data from various health tracking devices. It employs a RESTful architecture for seamless data retrieval, enabling users to obtain rich summaries that include sleep efficiency, heart rate variability (HRV), and stage breakdowns. The use of a centralized data model ensures consistency and accuracy across different metrics.
Unique: Utilizes a unified API endpoint to aggregate sleep data from multiple sources, ensuring comprehensive insights.
vs alternatives: More comprehensive than standalone sleep tracking apps due to its integration with various health metrics.
This capability provides real-time access to heart rate and heart rate variability (HRV) metrics by interfacing with Ultrahuman's backend services. It employs continuous data streaming techniques to ensure that users receive up-to-date information, which is crucial for effective training and recovery monitoring. The architecture supports high-frequency data updates, allowing for timely insights.
Unique: Incorporates real-time data streaming for heart metrics, allowing for immediate feedback and adjustments.
vs alternatives: More responsive than traditional heart rate monitors due to its integration with a broader health data ecosystem.
This capability tracks daily step counts by aggregating data from various fitness devices through Ultrahuman's API. It employs a data normalization process to ensure that step counts from different sources are comparable, providing users with a clear view of their daily activity levels. The system is designed to handle data from multiple devices, making it versatile for users with varied health tech.
Unique: Normalizes step data from multiple fitness devices, providing a unified view of user activity.
vs alternatives: Offers a more integrated approach than single-device step trackers by consolidating data from various sources.
This capability analyzes metabolic scores by pulling data from Ultrahuman's comprehensive health metrics database. It uses advanced algorithms to compute metabolic scores based on various inputs, including glucose levels and activity data. The architecture supports complex calculations and provides insights that can guide users in their wellness decisions.
Unique: Employs proprietary algorithms to compute metabolic scores, integrating multiple health metrics for holistic insights.
vs alternatives: Provides a more nuanced analysis than basic glucose monitors by factoring in activity and other health metrics.
This capability enables users to analyze trends over time by aggregating various health metrics into comprehensive reports. It utilizes data visualization techniques to present insights clearly and effectively, allowing users to identify patterns in their health data. The system supports custom reporting features, enabling users to tailor reports to their specific needs.
Unique: Combines multiple health metrics into a single reporting framework, enhancing the ability to track overall wellness trends.
vs alternatives: More comprehensive than basic reporting tools by integrating diverse health data into one platform.
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 ultrahuman at 30/100.
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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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