How to Use the Netrows MCP in LlamaIndex

Q: How do I load Netrows tools into LlamaIndex?

Install llama-index-tools-mcp and instantiate BasicMCPClient. Wrap it in McpToolSpec, call totoollistasync(), and feed the tools into your FunctionAgent.

Q: Can LlamaIndex cache Netrows flight schedules?

Yes. You execute getflightschedule for your target routes and embed the JSON responses into your vector database. The agent searches the local index first before hitting the live API again.

Q: How does LlamaIndex handle Netrows rate limits?

You index the output of getaccountusage. Before executing a large batch query for airport traffic, your agent checks the vector store for recent credit balances to avoid hitting the ceiling.

Index live aviation data into your LlamaIndex RAG applications using Netrows.

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Connect Netrows MCP to LlamaIndex

Create your Vinkius account to connect Netrows 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 Netrows with LlamaIndex

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Ground LlamaIndex queries in real fleet data

The `search_aircraft` tool extracts the complete list of registered planes operated by a specific airline. Your FunctionAgent pulls this fleet inventory and embeds it directly into your vector store. You enrich that index by calling `get_aircraft_info` on individual tail numbers. When users query your RAG application about plane ages or engine types, they get answers sourced from actual MCP Server data rather than outdated training weights.

Build searchable airline intelligence

The `get_airline_info` tool retrieves total fleet sizes, hub locations, and ICAO codes for global carriers. You index this metadata to give your agent a baseline understanding of airline operations. The system then layers on `get_airline_flights` from the MCP Server to index every active route that carrier is currently flying. Users run semantic searches against live operational patterns instead of static text documents.

Vectorize active flight telemetry

The `track_flight` tool streams real-time latitude, longitude, and ground speed for airborne planes. Your agent captures these telemetry points and stores them as temporal nodes in your index. You combine those coordinates with `get_flight_details` to add gate assignments and terminal data to the exact same vector. The user asks about a delayed arrival, and the system retrieves the exact spatial and logistical context instantly.

Setup guide

Set up Netrows 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 Netrows 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 Netrows tools.",
)
response = await agent.run("List recent Netrows data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Netrows. 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.

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Common questions about Netrows MCP in LlamaIndex

Install `llama-index-tools-mcp` and instantiate `BasicMCPClient`. Wrap it in `McpToolSpec`, call `to_tool_list_async()`, and feed the tools into your FunctionAgent.

Yes. You execute `get_flight_schedule` for your target routes and embed the JSON responses into your vector database. The agent searches the local index first before hitting the live API again.

You index the output of `get_account_usage`. Before executing a large batch query for airport traffic, your agent checks the vector store for recent credit balances to avoid hitting the ceiling.

No. The integration automatically converts the JSON responses into text nodes. You don't have to write manual data mapping logic for your RAG pipeline.

The endpoints only process public aviation telemetry and aircraft specifications. The zero-trust architecture ensures your internal RAG queries never leak back to the host, and all connections drop after execution.

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