How to Use the AeroDataBox MCP in LlamaIndex

Q: How do I register AeroDataBox tools inside a LlamaIndex agent?

You import llama-index-tools-mcp and initialize the basic client with your Vinkius URL. Convert the server's tools using McpToolSpec and pass them directly to your FunctionAgent. This exposes tools like getairlinefleet to your semantic search loop.

Q: Can I index historical flight data from AeroDataBox into a LlamaIndex vector store?

Yes, you can fetch historical records using getflighthistory and load the JSON payloads as LlamaIndex Document objects. Once indexed, your agent can perform semantic search across flight schedules. This reduces API credit usage by querying your local vector cache first.

Q: Can I limit which flight tools my LlamaIndex agent can access?

Yes, you can use the allowedtools filter during initialization to restrict access. For instance, you can expose only getairportrunways and block alert management tools. This keeps your agent focused on physical airport data without risking accidental credit spending.

Index live flight data and airport delay history directly into your LlamaIndex vector stores for semantic retrieval.

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Connect AeroDataBox MCP to LlamaIndex

Create your Vinkius account to connect AeroDataBox 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 AeroDataBox with LlamaIndex

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Index real-time flight data for RAG

This MCP Server connects live aviation tools like `get_flight_by_date` to your LlamaIndex pipeline so you can index real-time flight data. Your agent queries the flight status and immediately writes the status payload into a vector index. This allows users to run semantic search queries over current flight positions and schedules without hitting the API repeatedly. You can combine unstructured travel documents with live flight telemetry. When a user asks about travel disruptions, LlamaIndex pulls the latest stats from `get_airport_delays` and merges them with your internal travel policy PDFs. This delivers highly contextual answers grounded in actual airport conditions.

Search historical airport delays semantically

The historical analysis tools, specifically `get_airport_delays_historical` and `get_airport_delays_period`, let you build searchable archives of past airport performance in LlamaIndex using this MCP Server. Your pipeline fetches historical delay periods and indexes them as document nodes. This lets your agent run semantic queries to find patterns in winter weather disruptions. You can easily filter these indexed runs using metadata. LlamaIndex tags each indexed flight record with airport codes from `get_airport_routes_stats`. When your agent searches the vector store, it applies these metadata filters to isolate specific routes instantly.

Map flight distance calculations in LlamaIndex

The distance and time calculation tools, such as `get_distance_time`, allow your LlamaIndex agent to resolve geographic travel queries during retrieval steps using this MCP Server. If a user asks for alternative routes, the agent calls this tool to calculate flight durations. The resulting distances are then indexed to help rank the best travel options. This index is kept current using automated pipeline updates. Every time your agent runs a query, it can pull fresh coordinates using `get_airports_by_ip` to update the local vector store. This ensures your local search index stays geographically relevant to the user's physical location.

Setup guide

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

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Common questions about AeroDataBox MCP in LlamaIndex

You import `llama-index-tools-mcp` and initialize the basic client with your Vinkius URL. Convert the server's tools using `McpToolSpec` and pass them directly to your `FunctionAgent`. This exposes tools like `get_airline_fleet` to your semantic search loop.

Yes, you can fetch historical records using `get_flight_history` and load the JSON payloads as LlamaIndex Document objects. Once indexed, your agent can perform semantic search across flight schedules. This reduces API credit usage by querying your local vector cache first.

The McpToolAdapter automatically converts the output of tools like `get_fids_absolute` into structured dictionaries that LlamaIndex nodes parse easily. You don't need to write custom schemas. The agent reads the parsed flight data directly during its retrieval step.

Yes, you can use the `allowed_tools` filter during initialization to restrict access. For instance, you can expose only `get_airport_runways` and block alert management tools. This keeps your agent focused on physical airport data without risking accidental credit spending.

Your IP addresses, flight queries, and geographic search parameters are processed in a zero-trust V8 isolate. Vinkius does not store the flight numbers or airport codes your LlamaIndex agent requests. All data is wiped from memory as soon as the tool execution ends.

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