MeteoSource MCP. Audit Global Weather Forecasts from Conversation.
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MeteoSource connects your AI agent to global weather data. Use it to search for specific locations or find the nearest monitored spot using `get_nearest_weather_place`.
You can then audit detailed point forecasts, check timezones with `get_place_timezone`, and manage complex location metadata by searching places first with `search_weather_places` before running a forecast.
What your AI agents can do
Check api status
Checks if the MeteoSource service is currently operational and ready for use.
Get nearest weather place
Finds and returns the closest monitored weather station given a latitude and longitude.
Get place timezone
Retrieves timezone information for any specific, known place ID.
The agent searches a place name and returns a unique ID required for all other weather functions.
You provide latitude/longitude, and the tool identifies the nearest monitored weather location.
The agent takes a place ID and returns all associated timezone metadata (e.g., 'Asia/Tokyo').
You give the tool a place ID, and it retrieves current conditions, daily summaries, or hourly breakdowns.
The agent checks if the entire MeteoSource connection is currently online and working.
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MeteoSource: 5 Tools for Weather Data Access
These tools let your agent perform the full weather workflow: finding locations, checking nearby stations, retrieving timezones, getting point forecasts, and verifying API status.
019d8458check api status
Checks if the MeteoSource service is currently operational and ready for use.
019d8458get nearest weather place
Finds and returns the closest monitored weather station given a latitude and longitude.
019d8458get place timezone
Retrieves timezone information for any specific, known place ID.
019d8458get point forecast
Fetches detailed weather forecasts (current, daily, hourly) using a specific place ID.
019d8458search weather places
Searches for global locations by name and returns the necessary place IDs to run other tools.
Choose How to Get Started
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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What you can do with this MCP connector
MeteoSource plugs your AI agent straight into global weather data. You don't have to copy and paste from a dozen different websites. This server lets your agent run an entire meteorological workflow—from locating a place ID all the way through to generating hourly forecasts—all within the chat window.
It handles the complicated parts so you don't have to. Whether you start with coordinates or just a city name, your agent figures out what it needs. You can check time zones for specific places, pinpoint the closest monitored weather station, and pull detailed daily or hourly forecasts immediately.
First off, always run check_api_status. This tells you if the whole MeteoSource connection is online and actually working before you waste a shot trying to get data. If it's good to go, you can start finding locations. If you know what city you're looking for but don't have an official ID, run search_weather_places.
This searches global spots by name and spits out the unique place IDs required for every other function.
If you only have latitude and longitude—say, a GPS coordinate from a field worker—you use get_nearest_weather_place. It takes those coordinates and identifies the closest monitored weather station nearby. That tool gives you the ID of the spot you need to track.
Once you've got that place ID, you can build out your context. Need to know if an event is happening at sunrise in a different time zone? Run get_place_timezone with the place ID; it returns all the associated timezone metadata, like 'Asia/Tokyo'. This lets you synchronize events across multiple global locations.
Finally, when you need the weather itself, you use get_point_forecast. Just hand it that specific place ID. The tool pulls everything: current conditions summaries, full daily forecasts detailing high and low temperatures, or minute-by-minute hourly breakdowns. You get a complete picture of what's going down at that location.
Your agent uses these tools together—searching first, grabbing the timezone context second, then pulling the forecast third—all without you needing to know which ID belongs where. It’s clean data access for complex field operations.
How MeteoSource MCP Works
- 1 Subscribe to the server and give your AI client your unique MeteoSource API Key.
- 2 Call a tool (like
search_weather_places) with basic input, such as a city name or coordinates. - 3 The agent returns structured data—a place ID, timezone info, or forecast JSON—which you use for the next step.
The bottom line is: your agent handles the API calls and data parsing; you just talk to it normally.
Who Is MeteoSource MCP For?
Anyone who has to plan for real-world conditions—event planners, logistics leads, or climate researchers. Stop manually checking weather portals across time zones. If your job involves knowing what the sky looks like in a specific spot at 3 PM next Tuesday, this is for you.
Checking local forecasts and retrieving weather metadata to ensure outdoor event safety and timing.
Verifying regional conditions before shipping routes start, auditing potential weather impacts on deliveries.
Running rapid audits of point forecasts across different geographic regions using natural language queries.
What Changes When You Connect
- You get real-time weather data without leaving your chat window. Instead of opening five different websites, your agent calls
get_point_forecastand hands you the full JSON payload immediately. It’s all in one place. - Never worry about time zones again. Use
get_place_timezoneto instantly know if a location is GMT+9 or something else, which saves massive headaches for international event planning. - If you only have GPS coordinates, don't guess. Running
get_nearest_weather_placeaccurately pinpoints the correct monitored station, ensuring your forecast data is precise and localized. - Planning a regional audit? Use
search_weather_placesfirst to gather thousands of place IDs, then feed those bulk results intoget_point_forecastfor comprehensive oversight. - The whole system stays reliable. Before running any job, use
check_api_status. Knowing the API is up prevents your agent from failing mid-task and wasting time.
Real-World Use Cases
Prepping for a cross-country conference.
The coordinator needs to know if multiple cities will be impacted by rain. They ask the agent, which first uses search_weather_places to get IDs for Denver and Chicago. The agent then runs get_point_forecast on both sets of IDs, giving a side-by-side comparison so they can warn attendees before they even book flights.
Coordinating an outdoor film shoot.
The location scout is only given coordinates. They ask the agent to run get_nearest_weather_place. The agent finds the closest monitored station and then uses that place ID with get_point_forecast to get a detailed hourly breakdown, showing exactly when rain or wind might hit.
Auditing global supply chain timing.
The operations lead needs to know the time zone difference between two ports (e.g., Singapore and Seattle). They run get_place_timezone on both IDs, confirming the offset. Then they use that metadata to adjust delivery timelines for their team.
Building a global event calendar.
The planner needs to track weather data across 50 different locations. They run search_weather_places in batches, collect the place IDs, and then feed them into a loop that calls get_point_forecast for every single location, building an automated report.
The Tradeoffs
Assuming coordinates are enough.
The developer tries to run get_point_forecast directly with lat/long. The tool fails because it requires a place ID, not raw coordinates. This wastes time and forces manual lookups.
→
First, use get_nearest_weather_place to turn those rough coordinates into the required monitored place ID. Then pass that resulting ID to get_point_forecast. It's a two-step process.
Forgetting time zone checks.
The logistics team schedules a meeting based on local time, but fails to account for the difference between Asia/Tokyo and EST. They send invites that are off by hours.
→
Always run get_place_timezone first. This gives you the correct 'Asia/Tokyo' or 'America/New_York' identifier you need to build reliable, time-sensitive schedules.
Relying on a single forecast.
The user only checks the daily summary from get_point_forecast and plans an outdoor event, only for it to rain in the afternoon. The data wasn't granular enough.
→
When planning anything critical, always request the hourly breakdown by using get_point_forecast. This gives you a much higher resolution view of changing conditions.
When It Fits, When It Doesn't
Use MeteoSource if your core problem revolves around location-specific, time-sensitive weather data. Specifically: If you only have coordinates, use get_nearest_weather_place before anything else. If you need a list of possible locations, start with search_weather_places. If the forecast is the goal, always follow up by calling get_point_forecast using an ID obtained from one of the search tools. Don't use this if your problem is general data management or non-geographic information; for that, you need a different API type entirely.
Don’t run this server just because it has 'weather.' It’s not a generalized location database. If you only need to know the nearest gas station, using get_nearest_weather_place is overkill—you'd use a dedicated mapping service instead.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by MeteoSource. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 5 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Planning for weather used to mean opening five different websites.
Right now, if you need to audit weather across multiple locations or time zones, you’re stuck in a cycle of copy-pasting. You check Google Maps for coordinates, then jump to the forecast site and enter the place name, then open a third tab just to confirm the timezone offset. It's slow, it's error-prone, and if one website goes down, your whole plan stops.
With this server, you talk to your agent once. You tell it: 'Give me the forecast for Paris at 3 PM next Tuesday.' The agent handles all the dirty work—finding the right place ID via `search_weather_places`, checking the time zone with `get_place_timezone`, and finally running the detailed data through `get_point_forecast`. You get a clean answer, period.
MeteoSource: Get precise forecast data with `get_point_forecast`.
Manual forecasting checks involve guessing if the daily summary is enough. Are you planning an event? You need to know when the rain starts and stops, not just that it will rain sometime during the day. This means manually checking hourly graphs on multiple platforms.
Now, your agent uses `get_point_forecast` to pull the full breakdown instantly. It gives you the raw data needed for precise planning—the 3 PM forecast vs. the 6 PM forecast. The difference is moving from a vague 'rainy day' prediction to an actionable timeline.
Common Questions About MeteoSource MCP
How do I get a place ID using MeteoSource? +
Use search_weather_places. You just provide the name of the city or location, and the tool returns the necessary unique place IDs needed for all other forecast tools.
Is `get_nearest_weather_place` better than searching by name? +
Yes. If you only have latitude/longitude coordinates, get_nearest_weather_place is the right tool because it finds the closest monitored station to your raw point, guaranteeing data accuracy.
Can I check multiple time zones with MeteoSource? +
Yes. Run get_place_timezone for each location you care about. This lets your agent compile a list of all the required timezone metadata (like 'America/Los_Angeles') in one query.
What if I need to check the API status first? +
Start by calling check_api_status. This simple tool verifies the entire MeteoSource connection is live. It's smart practice before running any expensive or time-sensitive queries.
When I use `get_point_forecast`, what should my agent do if the Place ID is invalid? +
If get_point_forecast receives an invalid place ID, it will return a specific error code and no forecast data. Your client must implement basic input validation before calling this tool to ensure accuracy.
Are there rate limits when using multiple tools like `search_weather_places` and `get_nearest_weather_place`? +
Yes, the MeteoSource API enforces usage quotas. We recommend grouping related searches into fewer calls rather than running them in rapid succession to avoid hitting rate limits.
How does the data returned by `get_point_forecast` structure daily and hourly information? +
The forecast response is structured as a nested object containing both summary fields and an array for detailed breakdowns. You access the full temporal detail through the dedicated 'hourly' key within the main forecast body.
What kind of authentication setup does MeteoSource require when I connect via MCP? +
MeteoSource requires a unique API Key for all connections. You must securely store this key and pass it to your AI client's environment variables before running any tool.
How do I find my MeteoSource API Key? +
Sign up for a free account at meteosource.com, and you will find your API Key in your dashboard. Copy and paste it below.
Does it support location search by name? +
Yes. Use the search_weather_places tool providing the city or place name. Your agent will return the unique place_id required for forecasts.
Are hourly forecasts included? +
Yes. The get_point_forecast tool retrieves comprehensive weather metadata including current conditions, daily summaries, and hourly breakdowns.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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