| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification to expose weather data as callable tools/resources that Claude and other MCP-compatible clients can discover and invoke. Uses MCP's JSON-RPC 2.0 message transport layer with resource and tool definition schemas, enabling seamless integration into agentic workflows without custom API client code.
Unique: Implements MCP server specification for weather data, enabling Claude and other MCP clients to discover and call weather tools through standardized protocol rather than custom integrations — abstracts away API client complexity behind MCP's resource/tool schema system
vs alternatives: Provides protocol-standardized weather access vs. custom REST wrappers, allowing drop-in integration with any MCP-compatible LLM client without rewriting integration code
Accepts location input (city names, coordinates, or postal codes) and resolves them to weather data by querying an underlying weather provider API. Implements location normalization logic to handle ambiguous inputs (e.g., 'Springfield' → geographic disambiguation) and converts between coordinate systems and human-readable locations.
Unique: Abstracts location resolution complexity behind MCP tool interface, allowing Claude to handle natural language location inputs without explicit coordinate specification — implements location normalization and disambiguation at the protocol layer
vs alternatives: Simpler than raw weather API clients because location resolution is built-in and MCP-standardized, vs. requiring separate geocoding library integration
Fetches real-time weather observations (temperature, humidity, wind speed, precipitation, conditions) from upstream provider and converts between unit systems (Celsius/Fahrenheit, m/s/mph/knots) based on client preference. Implements unit conversion logic at the response layer to normalize output regardless of provider's native units.
Unique: Implements unit conversion at the MCP tool response layer, allowing clients to request weather in preferred units without managing conversion logic themselves — abstracts unit system complexity from the LLM client
vs alternatives: Cleaner than raw weather API clients because unit conversion is built-in and standardized, vs. requiring client-side conversion logic
Retrieves multi-day or hourly weather forecasts from upstream provider with configurable forecast length (e.g., 5-day, 10-day, hourly for next 48 hours). Implements time-based filtering and aggregation to return forecast data at requested granularity without requiring client-side parsing of full forecast datasets.
Unique: Exposes forecast data through MCP tool interface with configurable time horizons, allowing Claude to request specific forecast periods without parsing full provider datasets — implements time-based filtering at protocol layer
vs alternatives: More flexible than static forecast endpoints because clients can request custom time horizons and granularity, vs. fixed 5-day or 10-day forecast endpoints
Fetches active weather alerts, warnings, and advisories (tornado warnings, flood watches, heat advisories, etc.) for specified locations from upstream provider. Implements alert severity classification and filtering to surface critical warnings to LLM clients for inclusion in agent decision-making or user notifications.
Unique: Integrates severe weather alerts into MCP tool interface, enabling Claude agents to incorporate safety considerations into decision-making — abstracts alert severity classification and filtering from client logic
vs alternatives: Enables safety-aware agent behavior vs. weather-only APIs that ignore alerts, allowing agents to avoid recommending activities during dangerous conditions
Fetches air quality index (AQI), pollutant concentrations (PM2.5, PM10, O3, NO2, SO2, CO), and health recommendations from upstream provider. Implements AQI calculation and health impact classification to provide actionable air quality information for health-conscious applications and agents.
Unique: Exposes air quality data through MCP tool interface with health impact classification, enabling Claude agents to make health-aware recommendations — abstracts AQI calculation and pollutant interpretation from client logic
vs alternatives: More comprehensive than weather-only APIs because it includes environmental health factors, enabling agents to consider air quality in activity planning
Fetches UV index, solar radiation intensity, and sun exposure recommendations from upstream provider. Implements UV risk classification (low/moderate/high/very high/extreme) and provides skin protection recommendations based on UV index and skin type considerations.
Unique: Integrates UV index and solar radiation into MCP tool interface with health-aware risk classification, enabling Claude agents to provide sun safety recommendations — abstracts UV risk assessment from client logic
vs alternatives: Enables health-aware outdoor activity recommendations vs. weather-only APIs that ignore UV exposure risks
Fetches historical weather observations (temperature, precipitation, wind) for specified date ranges and locations from upstream provider. Implements time-series aggregation and statistical analysis (averages, extremes, trends) to support climate analysis and historical comparison use cases without requiring client-side data processing.
Unique: Exposes historical weather data through MCP tool interface with built-in statistical analysis, enabling Claude agents to perform climate analysis without external data processing — abstracts time-series aggregation from client logic
vs alternatives: More analytical than real-time weather APIs because it includes historical context and trend analysis, enabling agents to assess whether current conditions are anomalous
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-server at 24/100. weather-mcp-server 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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