How do I get an OpenAQ API Key?

Visit [**OpenAQ Platform**](https://platform.openaq.org/), create a free account, and generate your API key from the dashboard. Free tier includes 1,000 requests/hour.

What air quality parameters can I monitor?

PM2.5, PM10, O3 (ozone), NO2, SO2, CO, BC (black carbon), and more. Use `get_parameters` to see the full list with units and classifications.

Can I get historical air quality data?

Yes! Use `get_measurements` with date_from and date_to parameters in ISO 8601 format to retrieve time-series data for any location and parameter.

OpenAQ MCP Server for CrewAI 9 tools — connect in under 2 minutes

Name: OpenAQ
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

Connect your CrewAI agents to OpenAQ through the Vinkius — pass the Edge URL in the `mcps` parameter and every OpenAQ 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="OpenAQ Specialist",
    goal="Help users interact with OpenAQ effectively",
    backstory=(
        "You are an expert at leveraging OpenAQ 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 OpenAQ "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 available tools "
        "and what they can do."
    ),
)

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

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About OpenAQ MCP Server

Connect OpenAQ, the world's largest open air quality database, to any AI agent and monitor real-time pollution levels, track air quality trends, and access data from thousands of monitoring stations globally through natural language.

When paired with CrewAI, OpenAQ becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call OpenAQ 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 Monitoring — Get the latest PM2.5, O3, NO2, SO2, and CO readings from any location worldwide
Historical Analysis — Query time-series measurement data with date range filters for trend analysis
Location Discovery — Browse monitoring stations by country, city, or geographic area
Sensor Tracking — View active sensor devices and their measurement parameters
Parameter Reference — Look up all measurable air quality parameters with units and classifications
Global Coverage — Access data from 100+ countries with thousands of active monitoring locations

The OpenAQ MCP Server exposes 9 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 OpenAQ to CrewAI via MCP

Follow these steps to integrate the OpenAQ 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 9 tools from OpenAQ

Why Use CrewAI with the OpenAQ MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with OpenAQ 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

OpenAQ + CrewAI Use Cases

Practical scenarios where CrewAI combined with the OpenAQ MCP Server delivers measurable value.

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

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

OpenAQ MCP Tools for CrewAI (9)

These 9 tools become available when you connect OpenAQ to CrewAI via MCP:

get_countries

Includes location counts and city counts per country. List countries with monitoring stations

get_latest_measurements

Useful for getting current air quality status without querying full history. Get latest measurements per location

get_location_by_id

Get details for a specific location

get_locations

Filter by country, city, parameter, or geographic bounding box. Returns location details including coordinates, sensor counts, and whether the station is an official monitor. List air quality monitoring locations

get_measurements

Filter by location, parameter, date range, and value range. Returns readings with timestamps. Get historical air quality measurements

get_parameter_by_id

Get details for a specific parameter

get_parameters

5, PM10, O3 (ozone), NO2, SO2, CO, etc. Includes units and whether each is a core parameter. List measurable air quality parameters

get_sensor_by_id

Get details for a specific sensor

get_sensors

Filter by location, parameter type, or active status. List air quality sensors

Example Prompts for OpenAQ in CrewAI

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

"What's the current PM2.5 level in São Paulo, Brazil?"

"Which countries have the most air quality monitoring stations?"

"Show me ozone (O3) measurements from the last 24 hours in Paris."

Troubleshooting OpenAQ MCP Server with CrewAI

Common issues when connecting OpenAQ 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.

OpenAQ + CrewAI FAQ

Common questions about integrating OpenAQ 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.