Do I need an API key?

No! Open-Meteo is completely free and open-source for non-commercial use. No API key, no sign-up, no registration. Just subscribe and start querying. For commercial use, you'll need an API key from open-meteo.com.

What weather variables are available?

50+ variables including: temperature (2m, 80m, 120m, 180m), apparent temperature, humidity, dew point, wind speed/direction/gusts, precipitation, rain, snowfall, snow depth, cloud cover, pressure, UV index, sunshine duration, visibility, evapotranspiration, soil temperature/moisture and many more.

How far back does historical data go?

Historical weather data goes back to 1940 for most locations worldwide. Use get_historical_weather with start_date and end_date in YYYY-MM-DD format to retrieve archived data.

How do I find coordinates for a city?

Use get_geocoding with the city name (e.g. 'São Paulo', 'Tokyo', 'London'). Returns coordinates, elevation, timezone and country info. Then use those coordinates with get_forecast or get_historical_weather.

Open-Meteo MCP Server for CrewAI 5 tools — connect in under 2 minutes

Name: Open-Meteo
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 5 Tools Framework

Connect your CrewAI agents to Open-Meteo through Vinkius, pass the Edge URL in the `mcps` parameter and every Open-Meteo tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Open-Meteo Specialist",
    goal="Help users interact with Open-Meteo effectively",
    backstory=(
        "You are an expert at leveraging Open-Meteo tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Open-Meteo "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 5 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Open-Meteo MCP Server

Connect to Open-Meteo and access global weather forecasts through natural conversation — no API key needed.

When paired with CrewAI, Open-Meteo becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Open-Meteo tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Current Weather — Get real-time temperature, humidity, wind, precipitation and conditions
7-Day Forecast — Hourly and daily forecasts up to 16 days ahead with 50+ weather variables
Historical Weather — Access archived weather data going back to 1940 for any location
Air Quality — Get PM2.5, PM10, NO2, O3, SO2, CO and UV index forecasts
Geocoding — Find coordinates for any city or place name
Elevation — Get elevation data for any coordinates

The Open-Meteo MCP Server exposes 5 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Open-Meteo to CrewAI via MCP

Follow these steps to integrate the Open-Meteo MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 5 tools from Open-Meteo

Why Use CrewAI with the Open-Meteo MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Open-Meteo through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Open-Meteo + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Open-Meteo MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Open-Meteo for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Open-Meteo, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Open-Meteo tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Open-Meteo against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Open-Meteo MCP Tools for CrewAI (5)

These 5 tools become available when you connect Open-Meteo to CrewAI via MCP:

get_air_quality

5, PM10, nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide, dust, pollen and UV index. Requires latitude and longitude. Returns hourly data for up to 7 days. Common variables: pm2_5, pm10, nitrogen_dioxide, ozone, sulphur_dioxide, carbon_monoxide, dust, uv_index, alder_pollen, grass_pollen. Get air quality forecast for a location

get_elevation

Useful for hiking, aviation and geographic research. Get elevation for coordinates

get_forecast

Requires latitude and longitude. Supports hourly, daily and current weather variables. Common variables: temperature_2m, relative_humidity_2m, precipitation, rain, snowfall, wind_speed_10m, wind_direction_10m, wind_gusts_10m, weather_code, cloud_cover, pressure_msl, uv_index, visibility, apparent_temperature, dew_point_2m, sunshine_duration. Set past_days to include historical data (0-92 days). Set forecast_days for forecast length (0-16 days, default 7). Timezone defaults to GMT; use "auto" for local timezone. Get weather forecast for a location

get_geocoding

Useful for finding coordinates to use with weather tools. Returns up to 10 results by default. Find coordinates for a place name

get_historical_weather

Requires latitude, longitude, start date and end date (YYYY-MM-DD format). Supports the same hourly variables as the forecast API. Historical data goes back to 1940 for most locations. Use get_geocoding to find coordinates for a city name. Get historical weather data for a location

Example Prompts for Open-Meteo in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Open-Meteo immediately.

"What's the weather forecast for São Paulo this week?"

"What was the temperature in Tokyo on July 15, 2024?"

"What's the air quality in Beijing right now?"

Troubleshooting Open-Meteo MCP Server with CrewAI

Common issues when connecting Open-Meteo to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Open-Meteo + CrewAI FAQ

Common questions about integrating Open-Meteo MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.