{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_andyguo-andy-weather-mcp-server","slug":"andyguo-andy-weather-mcp-server","name":"andy-weather-mcp-server","type":"mcp","url":"https://smithery.ai/servers/andyguo/andy-weather-mcp-server","page_url":"https://unfragile.ai/andyguo-andy-weather-mcp-server","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","weather","smithery:andyguo/andy-weather-mcp-server"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_andyguo-andy-weather-mcp-server__cap_0","uri":"capability://tool.use.integration.weather.data.retrieval.via.mcp.protocol","name":"weather-data-retrieval-via-mcp-protocol","description":"Exposes 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.","intents":["I want Claude to check the weather for a specific location without leaving the conversation","I need to build an AI agent that can autonomously fetch weather data as part of a multi-step workflow","I want to integrate real-time weather information into my LLM application without writing custom API client code"],"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"],"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"],"requires":["MCP-compatible client (Claude via Claude Desktop, or other MCP-aware LLM interface)","Network connectivity to the weather data provider","API credentials for the underlying weather service (if required by the provider)","Node.js runtime (typical for MCP servers written in JavaScript/TypeScript)"],"input_types":["location (string: city name, coordinates, or postal code)","optional parameters (units: metric/imperial, language, forecast days)"],"output_types":["structured JSON with weather data (temperature, humidity, precipitation, wind speed, conditions)","forecast arrays with time-series meteorological predictions"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_andyguo-andy-weather-mcp-server__cap_1","uri":"capability://data.processing.analysis.location.based.weather.query.resolution","name":"location-based-weather-query-resolution","description":"Accepts 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.","intents":["I want to ask Claude for weather in a city without specifying exact coordinates","I need the agent to handle ambiguous location names and resolve them intelligently","I want to get weather for my current location using IP-based geolocation"],"best_for":["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"],"limitations":["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","Geocoding API calls add latency (~100-200ms) before weather data can be fetched","No persistent location history — each query must re-resolve the location unless cached"],"requires":["Geocoding service (built into weather API or separate service like Google Maps, Nominatim)","Location input from user or LLM context (city name, coordinates, or IP address)"],"input_types":["location string (city name, e.g., 'London')","coordinates (latitude/longitude tuple)","postal code (e.g., '90210')","IP address (optional, for geolocation)"],"output_types":["resolved coordinates (latitude, longitude)","canonical location name (e.g., 'London, United Kingdom')","weather data for resolved location"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_andyguo-andy-weather-mcp-server__cap_2","uri":"capability://data.processing.analysis.forecast.data.aggregation.and.formatting","name":"forecast-data-aggregation-and-formatting","description":"Retrieves 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.","intents":["I want Claude to tell me the weather forecast for the next week","I need an agent to compare weather conditions across multiple future time periods","I want to extract specific forecast metrics (e.g., rain probability, high/low temps) for decision-making"],"best_for":["Conversational weather assistants that provide multi-day forecasts","Planning agents that need to consider future weather conditions","Applications aggregating weather data for travel, event, or activity recommendations"],"limitations":["Forecast accuracy degrades beyond 7-10 days; longer forecasts are less reliable","Different weather providers have different forecast granularity (hourly vs. 3-hourly vs. daily) — normalization may lose precision","Forecast data can be large (100+ data points for 10-day hourly forecast) — may exceed LLM context limits if not filtered","Real-time forecast updates require frequent API calls; stale cached forecasts may be inaccurate"],"requires":["Weather API with forecast endpoint support","Specification of forecast duration (days or hours) and granularity"],"input_types":["location (resolved to coordinates)","forecast duration (e.g., '7 days', '24 hours')","optional granularity preference (hourly, daily)"],"output_types":["time-indexed forecast array with temperature, precipitation, wind, conditions for each period","aggregated summary (e.g., 'high of 75°F, 30% chance of rain')"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_andyguo-andy-weather-mcp-server__cap_3","uri":"capability://tool.use.integration.mcp.tool.schema.advertisement.and.discovery","name":"mcp-tool-schema-advertisement-and-discovery","description":"Implements 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.","intents":["I want Claude to automatically discover what weather queries are available from this server","I need the MCP client to validate my weather query parameters before sending them","I want to extend the server with new weather tools without modifying the client"],"best_for":["MCP server developers building tool-based integrations","Teams deploying multiple MCP servers and needing dynamic tool discovery","Builders creating extensible AI agents that discover available tools at runtime"],"limitations":["Schema advertisement is static — changes to tool definitions require server restart","No versioning mechanism for tool schemas — clients may cache outdated definitions","Schema complexity is limited by JSON Schema expressiveness — complex conditional parameters are difficult to represent","Tool discovery adds ~50ms latency on client startup as it fetches and parses all available tools"],"requires":["MCP-compatible client that supports tools/list and tools/call","JSON Schema definitions for each weather tool"],"input_types":["MCP protocol messages (tools/list request, tools/call request with tool name and parameters)"],"output_types":["tool metadata array with name, description, and JSON Schema for parameters","tool execution results (weather data or error messages)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_andyguo-andy-weather-mcp-server__cap_4","uri":"capability://safety.moderation.error.handling.and.fallback.responses","name":"error-handling-and-fallback-responses","description":"Implements 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.","intents":["I want Claude to handle weather API failures gracefully without crashing the conversation","I need the agent to retry failed weather queries automatically","I want informative error messages that help Claude understand what went wrong and suggest next steps"],"best_for":["Production AI applications that need resilience to external API failures","Agents that must continue operating even when weather data is unavailable","Teams building user-facing applications where API errors should be transparent"],"limitations":["Retry logic adds latency (exponential backoff can delay responses by several seconds)","Cached fallback data may be stale and inaccurate if the API has been down for hours","Rate limiting errors may require waiting before retrying — no way to predict when the API will be available again","Some errors (invalid location, malformed request) cannot be retried and require user intervention"],"requires":["Error handling logic in the MCP server implementation","Optional: caching layer for fallback data","Optional: retry configuration (max attempts, backoff strategy)"],"input_types":["failed API request (location, parameters)"],"output_types":["structured error response with error code, message, and suggested action","fallback data (cached weather) if available","retry indication if the error is transient"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_andyguo-andy-weather-mcp-server__cap_5","uri":"capability://tool.use.integration.mcp.client.compatibility.and.protocol.negotiation","name":"mcp-client-compatibility-and-protocol-negotiation","description":"Implements 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.","intents":["I want to use this weather server with Claude Desktop without custom integration code","I need this weather tool to work alongside other MCP servers in a multi-tool agent","I want to ensure my weather integration is future-proof as MCP evolves"],"best_for":["Teams standardizing on MCP for tool orchestration","Claude Desktop users wanting native weather integration","Developers building multi-tool agent systems"],"limitations":["Limited to MCP protocol version supported by server implementation — may lag behind latest spec","No fallback for non-MCP clients — requires MCP-compatible consumer","Protocol overhead adds latency compared to direct API calls"],"requires":["MCP-compatible client (Claude Desktop, MCP client SDK)","Proper MCP server discovery and configuration","Network connectivity between client and server"],"input_types":["MCP protocol messages (tool calls, resource requests)"],"output_types":["MCP protocol responses (tool results, resource data)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude via Claude Desktop, or other MCP-aware LLM interface)","Network connectivity to the weather data provider","API credentials for the underlying weather service (if required by the provider)","Node.js runtime (typical for MCP servers written in JavaScript/TypeScript)","Geocoding service (built into weather API or separate service like Google Maps, Nominatim)","Location input from user or LLM context (city name, coordinates, or IP address)","Weather API with forecast endpoint support","Specification of forecast duration (days or hours) and granularity","MCP-compatible client that supports tools/list and tools/call","JSON Schema definitions for each weather tool"],"failure_modes":["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","Geocoding API calls add latency (~100-200ms) before weather data can be fetched","No persistent location history — each query must re-resolve the location unless cached","Forecast accuracy degrades beyond 7-10 days; longer forecasts are less reliable","Different weather providers have different forecast granularity (hourly vs. 3-hourly vs. daily) — normalization may lose precision","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.42,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:29.348Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=andyguo-andy-weather-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=andyguo-andy-weather-mcp-server"}},"signature":"XIl2dvKN2Mi988A4KUDdVxvq2ziQiXYRnZ3H8aVTOWnqgVvRv4zD6wULG4oeFAhLqwHooJ2Bft7JrkMIhVjLBw==","signedAt":"2026-06-20T17:46:36.687Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/andyguo-andy-weather-mcp-server","artifact":"https://unfragile.ai/andyguo-andy-weather-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=andyguo-andy-weather-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}