Why choose the Full server instead of individual ones?

The Full server bundles **all 15 tools** from all 7 domain-specific servers into a single integration. Perfect for multi-disciplinary AI agents that need weather, climate, marine, air quality, flood, and location data without managing multiple connections.

Open-Meteo Full Access MCP Server for CrewAI 15 tools — connect in under 2 minutes

Name: Open-Meteo Full Access
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 15 Tools Framework

Connect your CrewAI agents to Open-Meteo Full Access through the Vinkius — pass the Edge URL in the `mcps` parameter and every Open-Meteo Full Access 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 Full Access Specialist",
    goal="Help users interact with Open-Meteo Full Access effectively",
    backstory=(
        "You are an expert at leveraging Open-Meteo Full Access 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 Full Access "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 15 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 Full Access MCP Server

The definitive Mega-Server for weather and climate intelligence. Why install 7 servers when one does it all?

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

What you can do

Live Weather — 16-day forecast + current conditions for any GPS coordinate
84 Years of History — Hourly weather archives from 1940 to today
Ocean Intelligence — Wave height, swell, currents, sea surface temperature at 5km
Air Safety — PM2.5, PM10, O₃, pollen counts, European & US AQI
Climate Future — IPCC projections to 2100 + ensemble probabilistic forecasts
Flood Risk — GloFAS river discharge with 40 years of reanalysis + 7 months forward
Location Tools — Global geocoding and 90m terrain elevation

The Open-Meteo Full Access MCP Server exposes 15 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 Full Access to CrewAI via MCP

Follow these steps to integrate the Open-Meteo Full Access 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 15 tools from Open-Meteo Full Access

Why Use CrewAI with the Open-Meteo Full Access MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Open-Meteo Full Access 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 the 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 Full Access + CrewAI Use Cases

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

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

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

Open-Meteo Full Access MCP Tools for CrewAI (15)

These 15 tools become available when you connect Open-Meteo Full Access to CrewAI via MCP:

get_air_quality

5, PM10, ozone, NO2, SO2, CO concentrations. Get air quality pollutant concentrations

get_aqi_index

Get AQI (European and US standards)

get_climate_projection

Get IPCC climate projections (2015–2100)

get_current_weather

Get current weather conditions

get_elevation

Get terrain elevation for any coordinates

get_ensemble_forecast

Get probabilistic multi-model ensemble forecast

get_flood_forecast

Get flood forecast up to 7 months ahead

get_historical_daily

Get historical daily aggregates

get_historical_weather

Covers 84 years. Get historical weather (1940–present)

get_marine_forecast

Get marine wave forecast at 5km resolution

get_ocean_currents

Get ocean currents and sea surface temperature

get_pollen_forecast

Get pollen and allergen forecast

get_river_discharge

Get river discharge data at 5km resolution

get_weather_forecast

Get weather forecast for any location (up to 16 days)

search_location

Search cities and locations globally

Example Prompts for Open-Meteo Full Access in CrewAI

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

"Full weather briefing for a yacht trip from Lisbon to Madeira next week"

"Climate risk assessment for a new data center in Singapore"

"What was the weather like on the day I was born? July 15, 1990 in Rome"

Troubleshooting Open-Meteo Full Access MCP Server with CrewAI

Common issues when connecting Open-Meteo Full Access 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

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

Open-Meteo Full Access + CrewAI FAQ

Common questions about integrating Open-Meteo Full Access 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.