| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows the weather_mcp server to retrieve real-time weather data by integrating with various weather APIs using the Model Context Protocol (MCP). It employs a modular architecture that enables seamless integration with multiple data sources, ensuring flexibility and scalability. The server can handle requests for specific weather conditions, forecasts, and historical data, returning structured responses that can be easily consumed by clients.
Unique: Utilizes the Model Context Protocol to standardize interactions with diverse weather APIs, allowing for easy extensibility and integration.
vs alternatives: More flexible than traditional weather APIs due to its modular MCP design, which allows for quick adaptation to new data sources.
This capability aggregates weather data from multiple APIs into a unified response format, leveraging the MCP's ability to handle diverse data structures. It employs a data normalization process that ensures consistency across different weather data sources, allowing developers to access a comprehensive view of weather conditions without worrying about the underlying API differences.
Unique: Employs a unique data normalization layer that standardizes responses from various weather APIs, facilitating easier integration.
vs alternatives: More efficient than single-source solutions, providing a broader data perspective without the need for complex client-side logic.
This capability allows users to set up customizable weather alerts based on specific conditions such as temperature thresholds, precipitation levels, or severe weather warnings. It uses a subscription model where users can register their preferences, and the server periodically checks the weather data against these criteria, sending notifications when conditions are met.
Unique: Incorporates a flexible subscription model that allows users to define specific alert conditions, enhancing user engagement.
vs alternatives: More user-friendly than static alert systems, providing tailored notifications based on individual preferences.
This capability enables users to query historical weather data by specifying date ranges and locations. The server interacts with weather data sources that maintain historical records, returning structured data that can be used for analysis or reporting. It employs efficient caching strategies to improve response times for frequently requested data.
Unique: Utilizes caching mechanisms to optimize retrieval of frequently accessed historical data, enhancing performance.
vs alternatives: Faster than traditional historical data APIs due to built-in caching and optimized querying strategies.
This capability allows the weather_mcp server to provide weather information based on user-defined locations, utilizing geolocation data to enhance the accuracy of weather reports. It integrates with geocoding services to convert user input into geographic coordinates, ensuring precise weather data retrieval.
Unique: Combines geocoding with weather data retrieval to provide highly accurate location-based weather reports.
vs alternatives: More precise than generic weather services, as it tailors responses based on exact user locations.
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 weather_mcp at 24/100. weather_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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