Meteostat Connector for AI agents.
10 live capabilities
Pull historical weather data and climate statistics for any location on Earth.
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Why people use Meteostat
Meteostat for Historical Weather Data Analysis
This Connector lets your agent do the work. You just tell it the city or coordinates, and it pulls the raw records directly into your workspace. You get clean data for your models or reports instantly.
What Vinkius changes
Your AI becomes a meteorology expert with instant access to global weather history.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Climate Modeling
A researcher needs to feed 10 years of daily temperature data into a model.
- Real-world use case 02
Logistics Planning
A freight company wants to know the weather patterns for a specific route in 2022.
- Real-world use case 03
Real Estate Analysis
An analyst wants to know if a city is getting hotter over time.
Complete set · 10capabilities
The complete Meteostat capability set.
These are the exact actions your AI can choose when you ask it to work with Meteostat.
01—04
4 capabilities in this set.
Part of 10 available through Meteostat.
- 01 Capability
Point monthly
Get historical monthly data for a specific coordinate. Use this when you need a monthly trend for a city without a station.
- 02 Capability
Point normals
Get 30-year climate normals for a point. This is perfect for understanding regional climate baselines.
- 03 Capability
Stations daily
Fetch daily statistics for a specific station. You can request up to 10 years of data at once.
- 04 Capability
Stations meta
Get metadata for a station using its ID, WMO, or ICAO code. Use this to identify specific stations in a region.
05—07
3 capabilities in this set.
Part of 10 available through Meteostat.
- 05 Capability
Stations monthly
Get historical monthly statistics for a specific station. This helps you see seasonal changes over several years.
- 06 Capability
Stations nearby
Find weather stations based on GPS coordinates and a radius. Use this to find the closest reliable data source.
- 07 Capability
Stations hourly
Pull hourly observations for a specific station. This is best for high-resolution data over a 30-day period.
08—10
3 capabilities in this set.
Part of 10 available through Meteostat.
- 08 Capability
Stations normals
Get the 30-year climate normals for a specific station. This provides a clear view of long-term weather averages.
- 09 Capability
Point daily
Get historical daily data for a specific coordinate. This works well for locations where no station exists nearby.
- 10 Capability
Point hourly
Get historical hourly data for a specific coordinate. Use this for precise hourly changes at any location on Earth.
Set up in minutes
One URL. Then ask Meteostat to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Meteostat from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Meteostat, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Meteostat for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Meteostat URL.
- Step 03
Save and start
Save the connection and enable Meteostat in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"meteostat": {
"url": "https://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Meteostat
Open Agent mode in chat and ask: "Using Meteostat, help me...". 10 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"meteostat": {
"url": "https://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Meteostat
Ask Copilot: "Using Meteostat, help me...". 10 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"meteostat": {
"url": "https://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Meteostat
Open Cascade and ask: "Using Meteostat, help me...". 10 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"meteostat": {
"url": "https://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Meteostat
Ask Cline: "Using Meteostat, help me...". 10 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add meteostat --transport http "https://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Meteostat
Ask Claude: "Using Meteostat, show me...". 10 tools are ready
Where the request belongs
Work Meteostat can move forward.
Data scientists who need to feed climate variables into models, researchers tracking environmental shifts, and logistics planners who need to see historical patterns to predict future risks.
Climate Researcher
Pulls 30-year averages to establish baselines for environmental impact studies.
Logistics Manager
Checks historical weather to find the safest routes for shipping during peak seasons.
Data Analyst
Feeds historical temperature sets into a machine learning model for energy demand forecasting.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsNCDC Climate Data Online
Access authoritative historical weather and climate data via NCDC. track datasets, stations, and climate records directly from your AI agent.
NCEI Climate Data Online (NOAA Archive)
Access historical weather and climate data from NOAA's National Centers for Environmental Information archive.
NOAA Climate — Historical Weather Records
Historical climate data from the planet's largest weather archive: GHCN-Daily temperature and precipitation records, monthly and yearly summaries, 30-year climate normals, and station search from NOAA's National Centers for Environmental Information.
Open-Meteo Historical Weather
Unlock 84 years of global weather history (1940–present): temperature, precipitation, wind, and snow data for any coordinate. the ultimate climate research companion.
Open-Meteo Full Access
The ultimate weather Mega-Server: 15 capabilities covering forecast, historical (84 years), marine, air quality, pollen, climate projections (to 2100), flood, geocoding, and elevation. all in one zero-auth integration.
AccuWeather
Access real-time weather data, detailed forecasts, and location-based climate insights directly from your AI agent using AccuWeather's global network.
Bring your own AI
Change the model, client or framework. Keep Meteostat connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Meteostat.
The practical details behind the request, access and result.
Can I get weather data for a place that doesn't have a weather station with the Meteostat MCP?
Yes. You can use point-based data which interpolates weather information for any specific coordinate on Earth, even in remote areas.
How far back does the historical data go with the Meteostat MCP?
The data depth depends on the specific station, but it often covers several decades of daily and monthly records.
Does the Meteostat MCP provide live weather updates?
No, this Connector is strictly for historical weather data and climate statistics. It does not provide real-time forecasts or current conditions.
What is the difference between daily and hourly data in the Meteostat MCP?
Hourly data provides a high-resolution look at temperature and precipitation changes throughout a single day, while daily data gives you a single summarized record for the 24-hour period.
Can I use the Meteostat MCP for commercial research projects?
Yes, you can use it to pull data for commercial analysis, though you should review the Meteostat terms of service regarding specific data redistribution.
How can I find weather data for a location that doesn't have a specific weather station?
You can use the point_hourly or point_daily capabilities. These capabilities use interpolation to calculate weather data for any geographic coordinate (latitude/longitude) by combining data from surrounding stations.
What is the difference between historical data and climate normals?
Historical capabilities like stations_daily provide actual observations for specific dates. The stations_normals capability provides long-term statistical averages (usually over 30 years), which represent the 'typical' weather for a location.
Can I get weather data in Fahrenheit instead of Celsius?
Yes. Most capabilities, such as stations_hourly and point_daily, include an optional units parameter. You can set this to 'imperial' to receive data in Fahrenheit and other non-metric units.
One connection away
Give your agent a direct line to Meteostat.
Connect Meteostat once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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