{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-knowair-weather","slug":"knowair-weather","name":"KnowAir Weather","type":"mcp","url":"https://github.com/shuowang-ai/Weather-MCP","page_url":"https://unfragile.ai/knowair-weather","categories":["mcp-servers","testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-knowair-weather__cap_0","uri":"capability://tool.use.integration.real.time.weather.data.retrieval.with.multi.standard.support","name":"real-time weather data retrieval with multi-standard support","description":"Fetches current weather conditions and forecasts from the Caiyun Weather API, supporting both Chinese meteorological standards and international formats. The MCP server acts as a standardized interface layer that abstracts the Caiyun API's response schema, enabling LLM agents to query weather data through a unified protocol without direct API credential management or response parsing logic.","intents":["I need to fetch current weather conditions for a specific location to include in an agent's decision-making","I want my LLM agent to access weather forecasts without managing API keys or handling raw JSON responses","I need weather data in both Chinese and international standards for multi-region applications"],"best_for":["LLM agents and AI applications requiring real-time weather context","Teams building weather-aware automation workflows in China and US markets","Developers integrating weather data into multi-modal reasoning systems"],"limitations":["Depends on Caiyun Weather API availability and rate limits — no built-in caching or fallback providers","Geographic coverage limited to regions supported by Caiyun (primarily China with US data available)","No historical weather data or long-term trend analysis — only current conditions and near-term forecasts"],"requires":["Caiyun Weather API key (free tier available with usage limits)","MCP client implementation (Claude Desktop, custom MCP host, or compatible framework)","Network connectivity to Caiyun API endpoints"],"input_types":["location coordinates (latitude/longitude)","location name or address string","optional parameters for forecast duration or data granularity"],"output_types":["structured JSON with temperature, humidity, wind speed, precipitation probability","forecast arrays with hourly or daily breakdowns","standardized field mappings for CN and US weather conventions"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-knowair-weather__cap_1","uri":"capability://data.processing.analysis.air.quality.index.aqi.monitoring.with.dual.standard.reporting","name":"air quality index (aqi) monitoring with dual-standard reporting","description":"Retrieves real-time air quality metrics from Caiyun Weather API, translating raw pollutant concentrations (PM2.5, PM10, O3, NO2, SO2, CO) into both Chinese Environmental Quality Standards (EQS) and US EPA AQI scales. The MCP server normalizes these standards into a unified response schema, allowing agents to reason about air quality across regulatory frameworks without manual conversion logic.","intents":["I need to check air quality levels for a location and get both Chinese and US standard interpretations","I want my agent to make health recommendations based on AQI data that complies with local regulations","I need to monitor pollution trends across multiple regions with consistent metric reporting"],"best_for":["Health and wellness applications operating in China and US markets","Environmental monitoring agents and compliance systems","Multi-region logistics or delivery optimization systems that factor air quality into routing"],"limitations":["AQI data granularity depends on Caiyun's sensor network — sparse coverage in rural areas","No historical AQI trends or predictive modeling — only current snapshot data","Conversion between CN and US standards is approximate; regulatory compliance requires official source verification"],"requires":["Caiyun Weather API key with AQI data access enabled","MCP client that supports structured tool responses with nested objects","Understanding of both CN EQS and US EPA AQI scales for proper interpretation"],"input_types":["location coordinates (latitude/longitude)","location name or city identifier"],"output_types":["structured JSON with individual pollutant concentrations (PM2.5, PM10, O3, NO2, SO2, CO)","AQI index values in both Chinese and US standards","health advisory strings mapped to AQI ranges"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-knowair-weather__cap_2","uri":"capability://tool.use.integration.mcp.protocol.endpoint.for.weather.and.aqi.tool.calling","name":"mcp protocol endpoint for weather and aqi tool calling","description":"Exposes weather and AQI data retrieval as standardized MCP tools that LLM agents can discover and invoke through the Model Context Protocol. The server implements MCP's tool schema definition and response marshaling, allowing Claude and other MCP-compatible clients to call weather/AQI functions as first-class tools without custom integration code. Handles credential management server-side, so agents never see raw API keys.","intents":["I want Claude or another LLM to autonomously fetch weather data as part of a multi-step reasoning task","I need to expose weather/AQI tools to an MCP client without writing custom tool-calling glue code","I want to run a local MCP server that agents can query for real-time environmental data"],"best_for":["Developers building Claude agents or other MCP-compatible LLM applications","Teams deploying local MCP servers for agent tool access without cloud dependencies","Organizations integrating weather/AQI into existing MCP-based agent frameworks"],"limitations":["MCP protocol overhead adds ~50-200ms latency per tool call vs. direct API calls","Requires MCP client implementation — not compatible with REST-only LLM frameworks","Tool discovery and schema validation happens at MCP handshake; schema changes require client reconnection"],"requires":["MCP-compatible client (Claude Desktop, custom MCP host, or framework with MCP support)","Python 3.8+ (typical for MCP server implementations)","Caiyun Weather API credentials configured in server environment"],"input_types":["MCP tool call requests with location parameters","tool discovery requests (MCP list_tools)"],"output_types":["MCP tool response objects with weather/AQI data","tool schema definitions (JSON Schema format)","error responses with structured error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-knowair-weather__cap_3","uri":"capability://memory.knowledge.location.based.weather.and.aqi.context.injection.for.agents","name":"location-based weather and aqi context injection for agents","description":"Enables LLM agents to automatically enrich their reasoning context with real-time weather and air quality data for specified locations. The MCP server retrieves and formats weather/AQI data in a way that agents can incorporate into their decision-making without explicit tool invocation — data can be pre-fetched and injected into system prompts or retrieved on-demand as part of tool-calling workflows. Supports batch location queries for multi-region scenarios.","intents":["I want my agent to automatically consider current weather when making recommendations (e.g., delivery routing, event planning)","I need to inject real-time AQI data into an agent's context for health-aware decision-making","I want to query weather/AQI for multiple locations in a single batch operation"],"best_for":["Multi-step agents that need environmental context for reasoning","Applications requiring location-aware recommendations (logistics, health, events)","Systems that batch-query multiple locations to reduce API call overhead"],"limitations":["Batch query performance depends on Caiyun API rate limits — no built-in request queuing or retry logic","Context injection adds token overhead to agent prompts — large location sets may exceed context windows","No caching between agent invocations — each query hits the live API"],"requires":["MCP client with support for tool-calling or context injection","Caiyun Weather API key","Agent framework that can consume structured weather/AQI data in prompts or tool responses"],"input_types":["single location (lat/lon or name)","array of locations for batch queries","optional parameters for data freshness or forecast depth"],"output_types":["formatted weather/AQI summary suitable for agent context","structured JSON with per-location weather and AQI metrics","human-readable strings for prompt injection"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-knowair-weather__cap_4","uri":"capability://data.processing.analysis.standardized.weather.data.schema.mapping.across.api.providers","name":"standardized weather data schema mapping across api providers","description":"Normalizes Caiyun Weather API responses into a consistent internal schema that abstracts provider-specific field names and data structures. The MCP server maps raw Caiyun fields (temperature, humidity, wind, precipitation) to standardized keys, enabling agents to work with weather data without learning provider-specific response formats. Schema includes both current conditions and forecast data with consistent temporal indexing.","intents":["I want my agent to work with weather data without needing to know Caiyun's specific response schema","I need consistent field names across weather queries so my agent logic doesn't break if I switch providers","I want to extend the weather tool to support multiple providers while maintaining a single agent interface"],"best_for":["Teams planning to support multiple weather API providers","Agents that need provider-agnostic weather data access","Applications requiring schema stability across API updates"],"limitations":["Schema normalization may lose provider-specific fields or precision — not all Caiyun data maps cleanly to standard fields","Currently only supports Caiyun as backend — multi-provider support would require additional adapter implementations","Schema versioning not explicitly handled — breaking changes to standard schema require client updates"],"requires":["Understanding of the normalized schema structure","Caiyun Weather API as the current backend provider"],"input_types":["raw Caiyun API response (JSON)"],"output_types":["normalized weather schema with standardized field names","structured JSON matching agent expectations"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Caiyun Weather API key (free tier available with usage limits)","MCP client implementation (Claude Desktop, custom MCP host, or compatible framework)","Network connectivity to Caiyun API endpoints","Caiyun Weather API key with AQI data access enabled","MCP client that supports structured tool responses with nested objects","Understanding of both CN EQS and US EPA AQI scales for proper interpretation","MCP-compatible client (Claude Desktop, custom MCP host, or framework with MCP support)","Python 3.8+ (typical for MCP server implementations)","Caiyun Weather API credentials configured in server environment","MCP client with support for tool-calling or context injection"],"failure_modes":["Depends on Caiyun Weather API availability and rate limits — no built-in caching or fallback providers","Geographic coverage limited to regions supported by Caiyun (primarily China with US data available)","No historical weather data or long-term trend analysis — only current conditions and near-term forecasts","AQI data granularity depends on Caiyun's sensor network — sparse coverage in rural areas","No historical AQI trends or predictive modeling — only current snapshot data","Conversion between CN and US standards is approximate; regulatory compliance requires official source verification","MCP protocol overhead adds ~50-200ms latency per tool call vs. direct API calls","Requires MCP client implementation — not compatible with REST-only LLM frameworks","Tool discovery and schema validation happens at MCP handshake; schema changes require client reconnection","Batch query performance depends on Caiyun API rate limits — no built-in request queuing or retry logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=knowair-weather","compare_url":"https://unfragile.ai/compare?artifact=knowair-weather"}},"signature":"A3+qbBrTTM/+XQ0AWF8izjdamOaenKkeWXNxaws40Y+8cTKoaGRJySEf6BEF48UQj70SzKTiKhsUmKrjgBybDg==","signedAt":"2026-06-23T05:23:58.532Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/knowair-weather","artifact":"https://unfragile.ai/knowair-weather","verify":"https://unfragile.ai/api/v1/verify?slug=knowair-weather","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"}}