{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_alok114-open-meteo-mcp","slug":"alok114-open-meteo-mcp","name":"open-meteo-mcp","type":"mcp","url":"https://github.com/Alok114/open-meteo-mcp","page_url":"https://unfragile.ai/alok114-open-meteo-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:Alok114/open-meteo-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_alok114-open-meteo-mcp__cap_0","uri":"capability://tool.use.integration.weather.data.retrieval.via.mcp.protocol","name":"weather-data-retrieval-via-mcp-protocol","description":"Exposes Open-Meteo weather API endpoints through the Model Context Protocol (MCP), allowing Claude and other MCP-compatible clients to query current weather, forecasts, and historical data without direct HTTP calls. Implements MCP resource and tool abstractions that translate natural language weather requests into Open-Meteo API parameters, handling coordinate lookup, unit conversion, and response formatting automatically.","intents":["I want Claude to fetch real-time weather data for any location without me managing API keys or HTTP requests","I need to build an AI agent that can answer weather questions by querying live meteorological data","I want to integrate weather context into LLM reasoning chains without writing boilerplate API integration code"],"best_for":["AI agent developers building weather-aware applications","Teams building Claude-powered assistants that need real-time weather context","Developers migrating from REST API calls to MCP-based tool integration"],"limitations":["Depends on Open-Meteo API availability and rate limits (no built-in caching or request batching)","No authentication required by Open-Meteo but MCP server itself has no access control — suitable only for trusted environments","Limited to Open-Meteo's supported geographic coverage and forecast models — cannot query proprietary weather services","Response latency includes both Open-Meteo API call time and MCP protocol serialization overhead"],"requires":["MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)","Network connectivity to Open-Meteo API endpoints (api.open-meteo.com)","Node.js runtime or equivalent (specific version depends on implementation — check repository)"],"input_types":["natural language location queries (e.g., 'weather in London')","structured geographic parameters (latitude, longitude, or ISO country codes)","forecast time ranges and weather variable specifications"],"output_types":["structured JSON weather data (temperature, precipitation, wind speed, etc.)","formatted text summaries suitable for LLM context","time-series forecast arrays with hourly or daily granularity"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alok114-open-meteo-mcp__cap_1","uri":"capability://data.processing.analysis.geographic.coordinate.resolution","name":"geographic-coordinate-resolution","description":"Translates human-readable location names (cities, regions, addresses) into latitude/longitude coordinates required by Open-Meteo's weather queries. Likely implements geocoding via Open-Meteo's Geocoding API or similar reverse-lookup service, handling ambiguous location names and returning ranked results so the LLM can disambiguate if needed.","intents":["I want to ask Claude for weather in 'New York' without manually specifying coordinates","I need the MCP server to automatically resolve city names to geographic coordinates for weather lookups","I want to handle ambiguous location names (e.g., multiple cities named 'Springfield') gracefully in weather queries"],"best_for":["Conversational AI agents that accept natural language location inputs","Applications where end-users provide city/region names rather than coordinates","Teams building multi-location weather dashboards or monitoring systems"],"limitations":["Geocoding accuracy depends on Open-Meteo's underlying geographic database — may not resolve very small towns or recent administrative boundary changes","Ambiguous location names return multiple candidates, requiring additional LLM reasoning to select the correct one (adds latency)","No support for custom location aliases or user-defined place names — always resolves against standard geographic databases"],"requires":["Access to Open-Meteo Geocoding API (free, no authentication)","Network connectivity to Open-Meteo endpoints"],"input_types":["location name strings (city, region, country)","partial or fuzzy location queries"],"output_types":["structured location objects with latitude, longitude, name, country, and confidence metadata","ranked lists of candidate locations for disambiguation"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alok114-open-meteo-mcp__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 Open-Meteo and formats them into structured, LLM-friendly representations. Handles variable selection (temperature, precipitation, wind, etc.), time-series aggregation, unit conversion (Celsius/Fahrenheit, metric/imperial), and response truncation to fit within LLM context windows. May implement caching of recent forecasts to reduce API calls.","intents":["I want Claude to provide a 7-day weather forecast for a location in a single, coherent response","I need hourly weather data formatted as a time-series that Claude can reason about and summarize","I want to specify which weather variables (temperature, rain, wind) to include in the forecast to save context tokens"],"best_for":["Weather chatbots and conversational assistants","Multi-day planning agents that need forecast context","Applications with strict context window limits that need selective weather data"],"limitations":["Forecast accuracy degrades beyond 10-14 days (Open-Meteo limitation, not MCP server)","No built-in caching — repeated queries for the same location/time incur full API latency","Large hourly forecasts (e.g., 30+ days × 24 hours) may exceed LLM context limits even after truncation","Unit conversion is applied uniformly — no per-variable unit customization"],"requires":["Open-Meteo API access","MCP client capable of handling structured JSON responses with nested time-series data"],"input_types":["geographic coordinates (latitude, longitude)","forecast duration (days or hours)","weather variable selection (temperature, precipitation, wind speed, etc.)","unit preferences (metric or imperial)"],"output_types":["structured forecast objects with time-indexed weather variables","formatted text summaries (e.g., 'High of 72°F, 30% chance of rain')","time-series arrays suitable for charting or further analysis"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alok114-open-meteo-mcp__cap_3","uri":"capability://data.processing.analysis.historical.weather.data.querying","name":"historical-weather-data-querying","description":"Provides access to historical weather records from Open-Meteo's archive, allowing queries for past weather conditions at any location and date. Implements time-range filtering, variable selection, and aggregation (daily, weekly, monthly summaries) to support weather analysis, trend detection, and climate pattern reasoning by LLMs.","intents":["I want Claude to analyze historical weather patterns for a location to inform climate or agricultural decisions","I need to retrieve past weather data for a specific date range to validate forecasts or investigate anomalies","I want to compare current conditions against historical averages to contextualize weather events"],"best_for":["Climate analysis and historical weather research agents","Agricultural or energy planning systems that need historical context","Anomaly detection systems comparing current conditions to historical baselines"],"limitations":["Historical data availability varies by location — some regions have limited archives (Open-Meteo limitation)","Queries spanning very long time ranges (e.g., 10+ years) return large datasets that may exceed context limits","No built-in statistical aggregation (e.g., percentiles, anomaly scores) — Claude must perform analysis on raw data","Historical data is read-only — no ability to correct or update records"],"requires":["Open-Meteo historical weather API access","Valid date ranges within Open-Meteo's archive coverage"],"input_types":["geographic coordinates","start and end dates (ISO 8601 format or natural language)","weather variables to retrieve","aggregation level (daily, weekly, monthly)"],"output_types":["time-series historical weather data","aggregated statistics (daily averages, min/max, totals)","formatted historical summaries for LLM analysis"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alok114-open-meteo-mcp__cap_4","uri":"capability://tool.use.integration.mcp.protocol.tool.registration.and.invocation","name":"mcp-protocol-tool-registration-and-invocation","description":"Implements the MCP server-side tool registration and invocation framework, exposing weather capabilities as MCP tools that Claude and other MCP clients can discover and call. Handles tool schema definition (parameters, return types), request routing, error handling, and response serialization according to the MCP specification. Likely uses an MCP SDK or framework (e.g., mcp-python, mcp-node) to manage the protocol handshake and message passing.","intents":["I want Claude to automatically discover available weather tools without manual configuration","I need the MCP server to handle tool invocation requests from Claude and return properly formatted responses","I want to add new weather capabilities (e.g., air quality, UV index) as MCP tools without rewriting the protocol layer"],"best_for":["MCP server developers building integrations with Claude or other LLM clients","Teams standardizing on MCP for AI tool integration across multiple services","Developers extending open-meteo-mcp with additional weather or environmental data sources"],"limitations":["MCP protocol overhead adds ~50-200ms per tool invocation compared to direct function calls","Tool schema must be statically defined at server startup — no dynamic tool generation based on runtime state","Error handling is limited to MCP error codes and messages — no custom exception types or detailed debugging info","No built-in authentication or authorization — MCP server assumes trusted clients only"],"requires":["MCP SDK compatible with the server's language (Node.js, Python, etc.)","MCP client that supports tool calling (Claude Desktop, custom MCP client)","Proper MCP server initialization and lifecycle management"],"input_types":["MCP tool call requests with structured parameters","MCP protocol messages (JSON-RPC format)"],"output_types":["MCP tool response messages with results or errors","tool schema definitions (JSON Schema format)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alok114-open-meteo-mcp__cap_5","uri":"capability://data.processing.analysis.unit.conversion.and.localization","name":"unit-conversion-and-localization","description":"Automatically converts weather data between unit systems (Celsius/Fahrenheit, metric/imperial wind speeds, millimeters/inches precipitation) and formats output according to user preferences or locale. Implements unit mapping tables and conversion logic within the response formatting pipeline, ensuring Claude receives data in the requested system without manual transformation.","intents":["I want weather data in Fahrenheit and imperial units for US-based users","I need the MCP server to automatically convert metric weather data to imperial for compatibility with legacy systems","I want to support multiple locales and unit preferences without hardcoding conversions in Claude's prompts"],"best_for":["Multi-region weather applications serving users with different unit preferences","Teams integrating weather data into systems expecting specific unit systems","Conversational agents that need to adapt output format to user locale"],"limitations":["Unit conversion is applied uniformly across all responses — no per-variable customization","Locale-specific formatting (e.g., date formats, number separators) may not align with all regional standards","Conversion precision is limited to typical weather reporting accuracy (e.g., 1°F, 1 mph) — not suitable for scientific applications requiring higher precision"],"requires":["Unit preference specification (metric or imperial)","Locale or region identifier for formatting"],"input_types":["unit system preference (metric, imperial, or mixed)","locale identifier (e.g., 'en-US', 'de-DE')"],"output_types":["weather data in requested unit system","formatted strings with locale-appropriate separators and symbols"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alok114-open-meteo-mcp__cap_6","uri":"capability://safety.moderation.error.handling.and.fallback.strategies","name":"error-handling-and-fallback-strategies","description":"Implements robust error handling for Open-Meteo API failures, network timeouts, invalid location queries, and out-of-range date requests. Provides meaningful error messages to Claude and may implement fallback strategies (e.g., returning cached data, degrading to lower-resolution forecasts, or suggesting alternative locations). Handles MCP protocol errors and ensures the server remains stable under adverse conditions.","intents":["I want Claude to gracefully handle weather API failures without crashing the conversation","I need informative error messages when a location cannot be resolved or data is unavailable","I want the MCP server to retry failed requests or use cached data when the API is temporarily unavailable"],"best_for":["Production weather agents that must handle API outages gracefully","Applications requiring high availability and resilience","Teams building user-facing weather assistants that need clear error communication"],"limitations":["Fallback strategies (e.g., cached data) may return stale information — no guarantee of freshness","Retry logic adds latency and may not recover from persistent API failures","Error messages are limited to MCP protocol constraints — cannot provide detailed debugging info to end-users","No built-in circuit breaker or rate-limiting to prevent cascading failures"],"requires":["Timeout configuration for API calls","Optional cache storage for fallback data","Error logging and monitoring infrastructure"],"input_types":["API responses (success or error)","network conditions and timeouts"],"output_types":["error messages suitable for LLM processing","fallback data (cached or degraded)","retry signals or circuit-breaker states"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alok114-open-meteo-mcp__cap_7","uri":"capability://planning.reasoning.weather.alert.and.extreme.condition.detection","name":"weather-alert-and-extreme-condition-detection","description":"Analyzes forecast data to identify extreme weather conditions (severe storms, heat waves, cold snaps, high wind, etc.) based on configurable thresholds. Implements condition detection logic that flags dangerous weather patterns and returns structured alerts with severity levels, allowing agents to proactively warn users or trigger contingency workflows. Detection rules can be customized per location or use case.","intents":["I want to be alerted if severe weather is forecast for my location","I need to identify heat waves or cold snaps in historical data","I want my agent to automatically cancel outdoor events if dangerous weather is predicted"],"best_for":["Safety-critical applications (emergency response, event planning)","Proactive notification systems that warn users of dangerous conditions","Agents that need to make weather-dependent decisions automatically"],"limitations":["Alert thresholds are static or require manual configuration; no machine learning-based anomaly detection","Detection is based on single-variable thresholds; complex multi-variable conditions require custom logic","No integration with official weather alerts (NWS, MeteoAlarm, etc.) — only Open-Meteo forecast data"],"requires":["Forecast data from Open-Meteo","Configurable alert threshold definitions (temperature, wind speed, precipitation, etc.)"],"input_types":["forecast data (JSON)","alert threshold configuration (object with min/max values for each variable)"],"output_types":["JSON alert array with severity levels, affected time periods, and variable values"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude Desktop, custom MCP client, or compatible LLM interface)","Network connectivity to Open-Meteo API endpoints (api.open-meteo.com)","Node.js runtime or equivalent (specific version depends on implementation — check repository)","Access to Open-Meteo Geocoding API (free, no authentication)","Network connectivity to Open-Meteo endpoints","Open-Meteo API access","MCP client capable of handling structured JSON responses with nested time-series data","Open-Meteo historical weather API access","Valid date ranges within Open-Meteo's archive coverage","MCP SDK compatible with the server's language (Node.js, Python, etc.)"],"failure_modes":["Depends on Open-Meteo API availability and rate limits (no built-in caching or request batching)","No authentication required by Open-Meteo but MCP server itself has no access control — suitable only for trusted environments","Limited to Open-Meteo's supported geographic coverage and forecast models — cannot query proprietary weather services","Response latency includes both Open-Meteo API call time and MCP protocol serialization overhead","Geocoding accuracy depends on Open-Meteo's underlying geographic database — may not resolve very small towns or recent administrative boundary changes","Ambiguous location names return multiple candidates, requiring additional LLM reasoning to select the correct one (adds latency)","No support for custom location aliases or user-defined place names — always resolves against standard geographic databases","Forecast accuracy degrades beyond 10-14 days (Open-Meteo limitation, not MCP server)","No built-in caching — repeated queries for the same location/time incur full API latency","Large hourly forecasts (e.g., 30+ days × 24 hours) may exceed LLM context limits even after truncation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.6,"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":"2026-05-03T15:19:06.728Z","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=alok114-open-meteo-mcp","compare_url":"https://unfragile.ai/compare?artifact=alok114-open-meteo-mcp"}},"signature":"6Mmm+vXaauhtokR8DnjVgTWveQmc0K02GABzSb2CxshBa8c6O1T9p5UmnyG3SlViCyV7ehtFM/TnlhNIpwUqAw==","signedAt":"2026-06-21T00:23:10.587Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alok114-open-meteo-mcp","artifact":"https://unfragile.ai/alok114-open-meteo-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=alok114-open-meteo-mcp","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"}}