How to Use the AQICN MCP in LlamaIndex

Q: Can I filter AQICN results in LlamaIndex?

You can use the allowedtools filter to restrict which functions the agent can access. This ensures the agent only queries the specific stations you want indexed.

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Connect AQICN MCP to LlamaIndex

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

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Setup AQICN with LlamaIndex

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Index AQICN feeds in LlamaIndex

Turn real-time air quality metrics into searchable knowledge by indexing tool outputs. Use `get_city_feed` to fetch current status and let your index store the results for semantic queries. Your RAG application gains the ability to answer questions about current conditions rather than relying on stale training data. The integration converts raw station reports into a queryable format.

Automated station discovery

Use `search_stations` to locate monitoring points and ingest their metadata into your LlamaIndex workflow. This keeps your document store updated with the latest station IDs. Once the stations are indexed, you can run semantic searches to find relevant sensors for specific regions. It turns a static list into a living part of your agent's memory.

Querying live weather and pollution

Pull live pollutant levels using `get_station_feed` to ground your LlamaIndex agent's responses in reality. The agent executes the tool and indexes the returned values for immediate use. This avoids hallucinations by forcing the model to look at current API data before forming an answer. Your index remains current because the tool runs exactly when the query requires it.

Setup guide

Set up AQICN MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all AQICN MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to AQICN tools.",
)
response = await agent.run("List recent AQICN data")

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Common questions about AQICN MCP in LlamaIndex

By indexing live API results, you provide the model with grounded facts instead of outdated guesses. The tool output becomes a part of the context the agent uses to answer user questions.

You can use the allowed_tools filter to restrict which functions the agent can access. This ensures the agent only queries the specific stations you want indexed.

It is straightforward. You initialize the BasicMCPClient with your URL and pass the tool spec to your FunctionAgent to get started.

Yes, the tool list is fully async. Use the provided async methods to ensure your RAG pipeline doesn't block while waiting for data.

The server transmits public air quality index figures over an encrypted connection. No personally identifiable information is ever processed or stored in your local index.

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