| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables Claude and other MCP-compatible AI models to establish bidirectional communication with Garmin wearables and fitness devices through the Model Context Protocol. Implements MCP server architecture that translates Garmin device APIs into standardized tool definitions, allowing language models to query real-time health metrics, activity data, and device status without direct API integration overhead.
Unique: Implements MCP server pattern specifically for Garmin ecosystem, providing standardized tool definitions that allow any MCP-compatible AI model to access Garmin data without custom integration code. Uses MCP's resource and tool abstractions to expose Garmin Connect API endpoints as discoverable, schema-validated capabilities.
vs alternatives: Simpler than building custom Garmin API integrations for each AI application; leverages MCP's standardized protocol to work with any MCP-compatible model rather than being locked to a single LLM provider
Provides structured access to current and historical activity data from paired Garmin devices including steps, heart rate, sleep metrics, stress levels, and workout summaries. Implements query patterns that map natural language requests to Garmin Connect API endpoints, returning parsed JSON responses with typed fields for metrics like calories burned, distance, elevation gain, and biometric data.
Unique: Abstracts Garmin Connect API complexity through MCP tool definitions, allowing natural language queries to be translated into structured API calls with automatic response parsing and field mapping. Handles pagination and multi-device scenarios transparently.
vs alternatives: More accessible than direct Garmin API integration because MCP handles authentication and response formatting; works with any MCP-compatible AI model without custom client code
Enables querying and managing multiple paired Garmin devices through a single MCP interface, providing device discovery, status monitoring, and device-specific capability detection. Implements device registry patterns that cache device metadata and capabilities, allowing AI models to understand which metrics are available per device and route queries appropriately.
Unique: Implements device registry and capability detection patterns within MCP framework, allowing AI models to understand device topology and make intelligent routing decisions. Caches device metadata to reduce API calls while maintaining freshness.
vs alternatives: Handles multi-device complexity transparently through MCP abstractions; simpler than building custom device management logic in each application
Leverages MCP's integration with Claude and other language models to provide natural language interpretation of Garmin metrics, translating raw numbers into actionable insights. Works by exposing structured fitness data through MCP tools, allowing the AI model's reasoning capabilities to analyze trends, identify patterns, and generate personalized health recommendations based on the retrieved data.
Unique: Combines MCP's tool-calling architecture with Claude's reasoning capabilities to enable sophisticated fitness data analysis without requiring custom analytics code. The AI model can iteratively query data and refine analysis through multi-turn conversations.
vs alternatives: More flexible than static analytics dashboards because Claude can reason about data contextually and adapt analysis based on user questions; simpler than building custom ML models for fitness trend detection
Integrates Garmin fitness data as contextual information within MCP's resource system, allowing AI agents to automatically consider user health status when making decisions or recommendations. Implements context injection patterns where relevant Garmin metrics are retrieved and included in the model context window, enabling agents to factor in current activity levels, sleep quality, stress levels, and recovery status into their reasoning.
Unique: Uses MCP's resource abstraction to make Garmin data available as persistent context that agents can reference, rather than requiring explicit tool calls for each decision. Enables seamless health-aware reasoning without cluttering the agent's tool namespace.
vs alternatives: More efficient than agents explicitly querying Garmin data for every decision because context is pre-fetched and injected; cleaner architecture than passing health data through custom agent state management
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 garmin-mcp at 22/100. garmin-mcp leads on ecosystem, while Supabase is stronger on quality.
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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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