Open-Meteo Flood & Rivers MCP for AI. Predicting River Flow and Disaster Risk, Not Just Today's Level.
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Open-Meteo Flood & Rivers connects your AI agent to global flood intelligence powered by GloFAS. It simulates river discharge at 5km resolution, predicts water levels up to seven months out, and accesses four decades of historical hydrological data (1984–present).
Stop guessing about floods; get actionable forecasts for disaster management.
What your AI can do
Get river discharge
Gets current, simulated river discharge measurements at a specific 5km resolution for real-time monitoring.
Get flood forecast
Retrieves projected river discharge rates up to 210 days in advance, useful for early flood warning systems.
Get historical discharge
Accesses reanalysis data covering river discharge from 1984 through the present day for trend analysis.
The agent predicts river discharge rates up to seven months ahead using the get_flood_forecast tool.
It retrieves real-time simulated river discharge data at 5km resolution via get_river_discharge.
The agent queries decades of archived flow data (1984–present) using get_historical_discharge to establish trend context.
It compares current river discharge against historical norms or projected peak flows from the forecast.
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Open-Meteo Flood & Rivers: 3 Tools for Hydrology Data Access
Access real-time, historical, and future river flow data. Run comprehensive flood risk assessments by combining three specialized hydrological models.
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Start using Open-Meteo Flood & Rivers on VinkiusGet River Discharge
Gets current, simulated river discharge measurements at a specific 5km resolution for real-time monitoring.
Get Flood Forecast
Retrieves projected river discharge rates up to 210 days in advance, useful for...
Get Historical Discharge
Accesses reanalysis data covering river discharge from 1984 through the present day...
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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 connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually compiling river data from multiple sources takes hours of clicking and cross-referencing spreadsheets.
Right now, running a proper flood risk assessment means pulling reports from an operational dashboard for current flow rates. Then you have to manually download historical climate summaries from an academic database to establish baseline trends. Finally, if you need to plan months out, you have to wait for the specialized modeling department to run their forecast models—a process that takes days and results in siloed PDFs.
With this MCP server, your agent handles it all instantly. You ask one question: 'What is the flood risk?' The system automatically calls `get_river_discharge` for real-time checks, pulls 40 years of context using `get_historical_discharge`, and generates a future projection with `get_flood_forecast`. You get actionable data in seconds.
The Open-Meteo Flood & Rivers MCP Server gives you flood intelligence by combining three specialized views.
Before, if a dam engineer needed to assess risk, they had to query the current flow using one tool, then manually request historical records from another system, and finally, run a separate forecast model. This workflow is slow, prone to human error, and requires specialized knowledge for every step.
Now, your agent handles the entire pipeline autonomously. You just tell it the goal—'Analyze flood risk at X location.' It coordinates all three tools (`get_river_discharge`, `get_historical_discharge`, and `get_flood_forecast`) to give you a single, comprehensive answer without you touching a dashboard or writing a complex script.
What your AI can actually do with this
Open-Meteo Flood & Rivers connects your AI agent to global flood intelligence, running on data from GloFAS. You get access to serious water resource modeling that stops you from guessing during a crisis.
Monitoring Current Flow Rates: When you need to know what's happening right now, the get_river_discharge tool pulls simulated river discharge measurements. This gives you current flow rates in cubic meters per second (m³/s) at a specific five-kilometer resolution. That granular detail means you can monitor exactly where the water is moving on your local grid.
Predicting Future River Levels: Need to know what's coming? The get_flood_forecast tool lets your agent look ahead, pulling projected river discharge rates up to 210 days out. This isn't just a rough estimate; it gives you actionable forecasts for proactive planning—whether you're dealing with seasonal spring melt or predicting the impact of extreme rainfall months in advance.
Analyzing Historical Baselines: To set context, you can run get_historical_discharge. This tool accesses reanalysis data covering river discharge from 1984 right up to the present day. You get four decades of archived flow data that lets you establish a solid trend baseline, letting you compare what's happening now against multi-decade historical patterns.
You can make your agent compare current readings against projected peak flows or even pit today's discharge rate against the average for this time of year over the last forty years. This capability is crucial; it turns raw measurements into predictive context.
The system lets you run get_river_discharge to get a real-time snapshot, then use that data point to feed into the get_flood_forecast, which projects forward for up to seven months. You can't just check current conditions; you must compare them against historical norms using get_historical_discharge to truly understand risk.
The high resolution of this platform is its strength. Getting discharge data at a five-kilometer grid point allows disaster managers to focus on specific river segments, not just general basin areas. This precision matters when timing evacuation routes or deploying resources. When you run the forecast, the agent projects the rates based on detailed hydrological modeling that considers everything from rainfall accumulation patterns to geographical bottlenecks.
The value isn't in having data; it's in using the three tools together. You check current flow with get_river_discharge, then use get_historical_discharge to benchmark against decades of reanalysis, and finally, you feed that whole picture into get_flood_forecast to see what happens over the next few months. This workflow gives you comprehensive flood intelligence for high-stakes water resource modeling.
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Here's how it actually works
The bottom line is that you get a comprehensive flood risk profile: where the river is now, where it's going, and how that compares to what happened before.
First, the agent checks get_river_discharge to establish the immediate flow rate and localized conditions.
Next, it calls get_flood_forecast to project how that river system might behave over the next few months (up to 210 days).
Finally, it runs get_historical_discharge to compare both the current reading and the forecast against established, multi-decade patterns.
Who is this actually for?
Anyone managing critical infrastructure or responding to natural hazards needs this. If your job involves water—whether predicting flood damage for insurance, designing drainage systems, or running operational emergency alerts—you need this server. It moves you past single-point monitoring and into full system risk assessment.
Uses get_river_discharge to verify current flow rates against design specifications, or uses historical data to model stress points.
Runs get_flood_forecast proactively to issue early warnings and allocate emergency resources months before a predicted flood peak.
Compares current trends using get_river_discharge against 40+ years of climate data via get_historical_discharge for research papers and long-term planning.
What Changes When You Connect
Early Warning Systems: Instead of reacting to water levels rising, you predict it. Use get_flood_forecast to set alerts weeks out for seasonal peaks or rapid rises.
Contextual Analysis: Don’t rely on a single reading. By comparing the current flow from get_river_discharge against four decades of data using get_historical_discharge, you determine if today's spike is normal or unprecedented.
Operational Planning: For infrastructure projects, knowing the expected peak flow 7 months out (via get_flood_forecast) lets engineers plan for maximum stress events, not just average conditions.
Comparison of Models: The server allows your agent to cross-reference three different data types—historical, real-time, and forecast—giving a far more reliable risk assessment than any single source.
Granular Detail: All measurements are provided at 5km resolution. You get specific localized data points that matter for civil engineering reports, not just city averages.
See it in action
Preparing for Monsoon Season
A disaster coordinator needs to know if the upcoming monsoon season poses a higher risk than usual. They run get_historical_discharge to see typical peak flows from 1984-2000, then use get_flood_forecast against current data from get_river_discharge to assess if this year's trajectory exceeds the historical maximum.
Insurance Underwriting
An underwriter needs to determine risk for a floodplain. They use get_historical_discharge to map out decades of flood intensity and then run get_flood_forecast to provide potential loss estimates based on the next 90-day projection.
Dam Management
A dam operator needs continuous flow data. They check get_river_discharge for immediate status, then use get_flood_forecast to model potential overtopping risks in the coming weeks and adjust release schedules accordingly.
Climate Change Modeling
An environmental researcher compares a local river's current discharge rate (get_river_discharge) against long-term historical trends spanning 40 years using get_historical_discharge to identify significant shifts in the baseline climate regime.
The honest tradeoffs
Assuming Current Data is Enough
A user only checks get_river_discharge and assumes everything is fine because the current flow rate is normal. This ignores seasonal shifts or upcoming weather patterns.
Always cross-reference the immediate reading with a forward look. Run get_flood_forecast and compare the results to historical context using get_historical_discharge before making any operational decisions.
Using Only Historical Averages
Relying solely on get_historical_discharge for planning. This assumes past conditions—like a 20-year drought or unprecedented rainfall—will repeat, which isn't guaranteed.
Use the historical data to set bounds, but always incorporate get_river_discharge and get_flood_forecast. The future prediction must be weighted by the current real-time status.
Ignoring Resolution Differences
Treating all data as uniform. Forgetting that some sources might cover different geographic areas or time scales, leading to localized errors.
Be specific about what you're asking for. If you need real-time monitoring, use get_river_discharge (5km resolution). If you need long-term planning, stick to the forecast tool.
When It Fits, When It Doesn't
Use this MCP Server if your primary concern is risk assessment requiring multi-variable data: specifically, when you need to compare what is happening now (get_river_discharge) against what is expected later (get_flood_forecast), while understanding the full scope of what has happened before (get_historical_discharge). You need a system that accounts for both current variability and long-term trends.
Don't use this if you just need simple, immediate weather reports (e.g., 'Is it raining right now?'). For those basic inputs, a standard meteorological API is better. Also, don't rely on the forecast alone; always confirm the prediction against the current reading to verify the model integrity.
Questions you might have
How far in advance can I predict floods using the get_flood_forecast tool? +
The get_flood_forecast tool provides predictions of river discharge up to 210 days (approximately seven months) out. This is useful for long-term seasonal planning.
Does get_river_discharge give me enough detail for local monitoring? +
Yes, get_river_discharge provides data at a 5km resolution, which is highly detailed and suitable for operational flood monitoring and water resource management.
What kind of data does get_historical_discharge provide? +
get_historical_discharge gives you reanalysis data covering river discharge from 1984 up to the present. This lets you compare current conditions against decades of recorded trends.
Can I use all three tools together in one prompt? +
Yes, your agent can coordinate all three. You can ask it to 'Compare today's discharge (using get_river_discharge) with the forecast (using get_flood_forecast), and show how that compares to 20 years ago (using get_historical_discharge).'
How do I specify a location when calling get_river_discharge? +
You must provide specific geographic boundaries or a recognized river ID to pinpoint the data. The tool requires this parameter to ensure it retrieves discharge values for your exact monitoring point.
What format does get_flood_forecast return its predictions in? +
The output is provided as structured JSON containing time-series data points, including the predicted discharge value (m³/s) and corresponding date. This format makes parsing straightforward for your AI client.
If I use get_historical_discharge, what happens if a specific year's records are missing? +
The tool handles gaps in the data gracefully by returning null values or an explicit error note. This prevents script failures and clearly flags incomplete historical datasets for your review.
Are there rate limits when frequently calling get_river_discharge? +
Vinkius manages standard API rate limiting to ensure system stability. If you exceed the allowed calls, you'll receive a 429 error code, prompting your agent to pause and retry later.
How accurate is the flood forecast? +
Data comes from GloFAS v4 (Global Flood Awareness System) by Copernicus at 5km resolution. Short-term forecasts (1-30 days) are highly reliable. Seasonal forecasts (1-7 months) indicate trends but have wider uncertainty bands.
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