andy-weather-mcp-server
MCP ServerFreeMCP server: andy-weather-mcp-server
Capabilities6 decomposed
weather-data-retrieval-via-mcp-protocol
Medium confidenceExposes weather data endpoints through the Model Context Protocol (MCP), allowing Claude and other MCP-compatible clients to query current conditions, forecasts, and meteorological data by invoking standardized MCP tools. The server implements MCP's tool definition schema to advertise available weather queries and marshals HTTP requests to an underlying weather API (likely OpenWeatherMap, WeatherAPI, or similar), returning structured JSON responses that conform to MCP's message format for seamless LLM integration.
Implements MCP's standardized tool protocol to expose weather queries, allowing Claude and other MCP clients to invoke weather lookups as first-class tools without custom integration code — the server handles protocol marshaling, schema advertisement, and response formatting automatically.
Simpler than building custom Claude plugins or REST API wrappers because MCP handles tool discovery and invocation semantics; more standardized than direct API calls because it works with any MCP-compatible client, not just Claude.
location-based-weather-query-resolution
Medium confidenceAccepts location identifiers (city names, geographic coordinates, postal codes, or IP addresses) and resolves them to weather data by performing geocoding or direct API lookups. The server likely implements location normalization logic to handle ambiguous inputs (e.g., 'Springfield' → user confirmation or default to most populous match) and may cache geocoding results to reduce redundant lookups, then passes resolved coordinates to the weather API.
Integrates location resolution directly into the MCP tool layer, allowing Claude to query weather by natural language location names without explicit coordinate specification — the server handles geocoding transparently as part of the tool invocation.
More user-friendly than requiring exact coordinates because it accepts natural language location names; more integrated than separate geocoding + weather API calls because resolution happens within a single MCP tool invocation.
forecast-data-aggregation-and-formatting
Medium confidenceRetrieves multi-day or hourly weather forecasts from the underlying API and formats them into a structured, LLM-friendly format (likely JSON arrays with time-indexed weather objects). The server may implement filtering logic to return only relevant forecast periods (e.g., next 7 days, next 24 hours) and may normalize units and field names across different weather API providers to ensure consistent output schema.
Normalizes forecast data from the underlying weather API into a consistent, LLM-optimized JSON schema, abstracting away provider-specific field names and units so Claude receives uniform forecast data regardless of the backend service.
More LLM-friendly than raw API responses because it formats forecasts as structured arrays with consistent field names; more concise than full API responses because it filters to relevant time periods and omits redundant metadata.
mcp-tool-schema-advertisement-and-discovery
Medium confidenceImplements MCP's tools/list and tools/call endpoints to advertise available weather query capabilities and their parameters to MCP clients. The server defines JSON Schema for each tool (e.g., 'get_weather' with location and units parameters) and responds to client discovery requests with tool metadata, enabling Claude and other clients to understand what weather queries are available and what parameters they accept without hardcoding tool definitions.
Implements MCP's standardized tool discovery protocol, allowing clients to dynamically discover available weather tools and their parameter schemas at runtime — no hardcoding of tool definitions needed on the client side.
More flexible than REST API documentation because tool schemas are machine-readable and discoverable; more standardized than custom tool registries because it uses MCP's official protocol.
error-handling-and-fallback-responses
Medium confidenceImplements error handling for common failure modes (API unavailability, invalid locations, rate limiting, network timeouts) and returns structured error responses via MCP that Claude can interpret and act upon. The server may implement retry logic with exponential backoff for transient failures, graceful degradation (e.g., returning cached data if API is down), or informative error messages that help the LLM understand why a query failed and suggest alternatives.
Implements MCP-compliant error responses that Claude can interpret as structured failures, allowing the LLM to understand why a weather query failed and decide whether to retry, use cached data, or inform the user.
More robust than simple error propagation because it includes retry logic and fallback strategies; more LLM-friendly than raw HTTP errors because it returns structured MCP error messages that Claude can parse and act upon.
mcp-client-compatibility-and-protocol-negotiation
Medium confidenceImplements the full MCP server protocol specification, enabling compatibility with any MCP-compliant client (Claude Desktop, custom agents, multi-tool orchestrators). The server handles MCP protocol handshakes, capability negotiation, and request/response marshaling, abstracting away protocol details from weather data logic.
Fully implements MCP server specification, enabling plug-and-play integration with any MCP client without custom protocol adapters or client-specific code
More portable than custom REST or gRPC endpoints because MCP standardizes the protocol, allowing the same server to work with multiple clients and agent frameworks
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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Weather
Get location-based forecasts and real-time US weather alerts. Plan your day with precise, up-to-date conditions at any location. Stay safe with timely warnings for severe weather.
Best For
- ✓AI application developers building Claude-powered agents that need environmental context
- ✓Teams integrating weather data into conversational AI systems
- ✓Builders prototyping weather-aware autonomous agents
- ✓Conversational AI applications where users provide natural language location names
- ✓Multi-location weather monitoring agents that need to disambiguate city names
- ✓Applications serving international users with varying location naming conventions
- ✓Conversational weather assistants that provide multi-day forecasts
- ✓Planning agents that need to consider future weather conditions
Known Limitations
- ⚠Dependent on upstream weather API availability and rate limits — if the underlying service is down or rate-limited, queries fail
- ⚠No built-in caching or local weather database — every query hits the remote API, adding latency
- ⚠Limited to weather data only — cannot provide other contextual data types without extending the server
- ⚠MCP protocol overhead adds ~50-100ms per request compared to direct API calls
- ⚠Ambiguous location names (e.g., 'Springfield' exists in 30+ US states) may require fallback logic or user clarification
- ⚠IP-based geolocation is approximate (city-level accuracy, not street-level) and may fail for VPN/proxy users
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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