How to Use the AirVisual MCP in AutoGen

Q: How do I configure the AirVisual MCP tools for AutoGen?

Run pip install -U "autogen-extmcp". Use mcpservertools with StreamableHttpServerParams to fetch the tool definitions from your Vinkius endpoint.

Q: Can multiple AutoGen agents use the AirVisual MCP Server simultaneously?

You pass the tool list to specific AssistantAgent constructors. Only the agents equipped with those tools can execute operations like getstationdata.

Q: How does AutoGen convert the AirVisual tool schemas?

The McpToolAdapter handles all schema translations automatically. Your agents see standard Python functions when calling getcitydata.

Give your AutoGen agents this MCP Server to debate real-time global air quality.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect AirVisual MCP to AutoGen

Create your Vinkius account to connect AirVisual to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup AirVisual with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Equip AutoGen MCP Server Teams

AirVisual feeds live atmospheric readings directly into your multi-agent debates. A data-gathering agent runs `get_city_data` to pull the raw AQI numbers before passing them to an analysis agent for interpretation. Different agents take different approaches to the same environmental problem. One might use `get_nearest_city_by_ip` for quick local context, while another challenges that finding by pulling exact coordinates.

Debate Pollution Metrics

Your agents argue over the severity of weather conditions using hard data. A health-focused agent flags high PM2.5 levels from `get_station_data`, while a logistics agent argues the wind patterns remain safe for transport. Consensus emerges from verifiable API calls rather than assumed conditions. The team cross-references regional data using `list_states` and `list_cities` until they agree on a final recommendation.

Automate Spatial Verification

Agents independently verify location data before making decisions. If a user reports poor air quality, a verification agent executes `get_nearest_city_by_coords` to confirm the exact local readings. The conversation naturally routes through the geographic hierarchy. An agent tasked with a global report will methodically loop through `list_countries` to assign regional analysis tasks to its peers.

Setup guide

Set up AirVisual MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes AirVisual tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="AirVisual_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent AirVisual data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="AirVisual_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent AirVisual data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AirVisual. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect AirVisual now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about AirVisual MCP in AutoGen

Run `pip install -U "autogen-ext[mcp]"`. Use `mcp_server_tools` with `StreamableHttpServerParams` to fetch the tool definitions from your Vinkius endpoint.

You pass the tool list to specific `AssistantAgent` constructors. Only the agents equipped with those tools can execute operations like `get_station_data`.

The `McpToolAdapter` handles all schema translations automatically. Your agents see standard Python functions when calling `get_city_data`.

That depends on your system prompt. You can instruct a safety agent to halt operations if the pollution levels exceed a certain threshold, forcing a negotiation with other agents.

Any IP or GPS coordinate passed to the server hits an ephemeral V8 isolate. Vinkius processes the location to fetch the matching AQI station and immediately destroys the sandbox state.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript