# 🌤️ Weather MCP — AI Forecast & Climate Data (`nexgendata/weather-mcp-server`) Actor MCP server for AI assistants to get weather forecasts, historical climate data, severe weather alerts, and location-based conditions. Works with Claude, Cursor, and any MCP-compatible client. - **URL**: https://apify.com/nexgendata/weather-mcp-server.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** AI, Automation, MCP servers - **Stats:** 0 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 🌤️ Weather MCP — AI Forecast & Climate Data > MCP server for AI assistants to get weather forecasts, historical climate data, severe weather alerts, and location-based conditions. Works with Claude, Cursor, and any MCP-compatible client. ### 🔑 Features - Runs in the Apify cloud — no local setup, no maintenance - Structured JSON output ready for analytics, CRMs, or databases - Batch-friendly — process many inputs in one run - Integrates with Zapier, Make.com, n8n, Google Sheets, Slack, and direct API calls - Pay-per-use pricing — no subscription, no monthly minimum ### 💼 Common Use Cases - Market research and competitive intelligence - Data enrichment and lead generation - Content monitoring and automated alerts - Pipeline and integration workflows - Dashboards and reporting ## Weather Forecast MCP Server MCP server for weather forecasts and conditions by location. Connect via MCP: `https://nexgendata--weather-mcp-server.apify.actor/mcp` --- ### 💻 Code Example — Python ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("nexgendata/weather-mcp-server").call(run_input={ ## Fill in the input shape from the actor's input_schema }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ```` ### 🌐 Code Example — cURL ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~weather-mcp-server/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ /* input schema */ }' ``` ### ❓ FAQ **Q: How do I get started?** Sign up at [apify.com](https://www.apify.com/?fpr=2ayu9b), grab your API token from Settings → Integrations, and run the actor via the Apify console, API, Python SDK, or any integration (Zapier, Make.com, n8n). **Q: What's the typical cost per run?** See the pricing section below. Most runs finish under $0.10 for typical batches. **Q: Is this actor maintained?** Yes. NexGenData maintains 140+ Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get responses within 24 hours. **Q: Can I use the output commercially?** Yes — you own the output data. Check the target site's Terms of Service for any usage restrictions on the scraped content itself. **Q: How do I handle rate limits?** Apify manages concurrency and retries automatically. For very large batches (10K+ items), run multiple smaller jobs in parallel instead of one mega-job for better reliability. ### 💰 Pricing Pay-per-event pricing — you only pay for what you actually extract. - **Actor Start:** $0.0001 - **result:** $0.0020 ### 🔗 Related NexGenData Actors - [RAG Web Browser](https://apify.com/nexgendata/rag-web-browser?fpr=2ayu9b) - [AI Web Scraper](https://apify.com/nexgendata/ai-web-scraper?fpr=2ayu9b) - [Hacker News Scraper](https://apify.com/nexgendata/hacker-news-scraper?fpr=2ayu9b) ### 🚀 Apify Affiliate Program New to Apify? Sign up with our [referral link](https://www.apify.com/?fpr=2ayu9b) — you get free platform credits on signup, and you help fund the maintenance of this actor fleet. *** *Built and maintained by [NexGenData](https://apify.com/nexgendata?fpr=2ayu9b) — 140+ actors covering scraping, enrichment, MCP servers, and automation.* # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/weather-mcp-server").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("nexgendata/weather-mcp-server").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call nexgendata/weather-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/weather-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🌤️ Weather MCP — AI Forecast & Climate Data", "description": "MCP server for AI assistants to get weather forecasts, historical climate data, severe weather alerts, and location-based conditions. Works with Claude, Cursor, and any MCP-compatible client.", "version": "0.0", "x-build-id": "zuSlfqPErnNCEfcSK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~weather-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-weather-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~weather-mcp-server/runs": { "post": { "operationId": "runs-sync-nexgendata-weather-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~weather-mcp-server/run-sync": { "post": { "operationId": "run-sync-nexgendata-weather-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": {} }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```