How far back does the historical data go?

All the way to **January 1, 1940** — that's 84 years of continuous, hourly global weather data powered by ERA5 reanalysis from ECMWF.

Open-Meteo Historical Weather MCP Server for CrewAI 3 tools — connect in under 2 minutes

Name: Open-Meteo Historical Weather
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 3 Tools Framework

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

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Open-Meteo Historical Weather Specialist",
    goal="Help users interact with Open-Meteo Historical Weather effectively",
    backstory=(
        "You are an expert at leveraging Open-Meteo Historical Weather 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 Historical Weather "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 3 available tools "
        "and what they can do."
    ),
)

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

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About Open-Meteo Historical Weather MCP Server

Access 84 years of continuous weather records from 1940 to today for any location on Earth.

When paired with CrewAI, Open-Meteo Historical Weather becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Open-Meteo Historical Weather 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

Historical Hourly — Temperature, humidity, precipitation, snowfall, weather codes, and wind for any past date range
Historical Daily — Max/min temperatures, precipitation totals, sunshine duration, and dominant wind patterns
Temperature Trends — Dedicated tool for long-term climate trend analysis with apparent temperature data

The Open-Meteo Historical Weather MCP Server exposes 3 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 Historical Weather to CrewAI via MCP

Follow these steps to integrate the Open-Meteo Historical Weather 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 3 tools from Open-Meteo Historical Weather

Why Use CrewAI with the Open-Meteo Historical Weather MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Open-Meteo Historical Weather 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 Historical Weather + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Open-Meteo Historical Weather 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 Historical Weather, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Open-Meteo Historical Weather 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 Historical Weather against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Open-Meteo Historical Weather MCP Tools for CrewAI (3)

These 3 tools become available when you connect Open-Meteo Historical Weather to CrewAI via MCP:

get_historical_daily

Get historical daily weather aggregates

get_historical_temperature

Includes hourly temperature, apparent temperature, and dewpoint. Get historical temperature trends for climate analysis

get_historical_weather

Provide latitude, longitude, start_date and end_date in YYYY-MM-DD format. Covers 84 years of global data. Get historical weather for any date range (1940–present)

Example Prompts for Open-Meteo Historical Weather in CrewAI

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

"What was the weather in London on D-Day, June 6, 1944?"

"Compare average temperatures in São Paulo between 1950 and 2020"

"How much rain fell in Mumbai during the 2005 flood?"

Troubleshooting Open-Meteo Historical Weather MCP Server with CrewAI

Common issues when connecting Open-Meteo Historical Weather 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 Historical Weather + CrewAI FAQ

Common questions about integrating Open-Meteo Historical Weather 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.