How to Use the NASA Asteroids — Near-Earth Objects & Planetary Defense MCP in LlamaIndex

Q: How do I configure NASA Asteroids — Near-Earth Objects & Planetary Defense with LlamaIndex?

Install llama-index-tools-mcp. Use BasicMCPClient to connect, then wrap it with McpToolSpec(client=mcpclient). Call totoollistasync() to pass the tools to your FunctionAgent.

Q: Can LlamaIndex store the results from NASA Asteroids — Near-Earth Objects & Planetary Defense?

That is the primary use case. You can execute getcloseapproaches, take the resulting JSON, and embed it into your vector database for future semantic search.

Index near-Earth object data and fireball events into searchable LlamaIndex vector stores.

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Connect NASA Asteroids — Near-Earth Objects & Planetary Defense MCP to LlamaIndex

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Setup NASA Asteroids — Near-Earth Objects & Planetary Defense with LlamaIndex

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Claude

Perplexity

Ingest the complete asteroid catalog.

`get_neo_browse` pulls 20 asteroids per page from the complete known catalog so your LlamaIndex pipeline can ingest them. You run a scheduled job that pages through the database, embedding orbital parameters and physical characteristics into your vector store. Once indexed, your agent stops guessing about space rocks. You can ask natural language questions about historical asteroid classifications, and the system retrieves answers grounded in actual NASA NeoWs records.

Cross-reference live feeds with past impacts.

`get_neo_feed` and `get_close_approaches` supply live data on upcoming encounters that you can append to your existing knowledge base. When an asteroid triggers a hazard warning, your LlamaIndex application fetches the diameter, velocity, and miss distance. The agent cross-references this live feed against your vector store of past impacts. You build an application that compares tomorrow's close passes with similar events from a decade ago, relying entirely on factual JPL CNEOS data.

Build a queryable fireball history.

`get_fireballs` feeds atmospheric bolide events—including location, altitude, and kiloton energy yield—directly into your RAG architecture. You turn raw US government sensor data into a queryable history of Earth impacts. Your users do not have to parse complex API responses. They just ask the agent for the largest atmospheric explosion over the Pacific last year, and LlamaIndex retrieves the exact fireball record from the index.

Setup guide

Set up NASA Asteroids — Near-Earth Objects & Planetary Defense 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 NASA Asteroids — Near-Earth Objects & Planetary Defense 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 NASA Asteroids — Near-Earth Objects & Planetary Defense tools.",
)
response = await agent.run("List recent NASA Asteroids — Near-Earth Objects & Planetary Defense data")

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Common questions about NASA Asteroids — Near-Earth Objects & Planetary Defense MCP in LlamaIndex

Install `llama-index-tools-mcp`. Use `BasicMCPClient` to connect, then wrap it with `McpToolSpec(client=mcp_client)`. Call `to_tool_list_async()` to pass the tools to your `FunctionAgent`.

That is the primary use case. You can execute `get_close_approaches`, take the resulting JSON, and embed it into your vector database for future semantic search.

When a user queries a specific asteroid by name or SPK-ID, the agent calls `get_neo_lookup` to fetch the real-time profile. It then synthesizes that live data with whatever historical context you already indexed.

No. LlamaIndex grounds its responses in the exact JSON returned by the tools. If `get_neo_feed` says the miss distance is 0.05 AU, the agent repeats that exact figure.

The server exclusively queries external astronomical databases for asteroid diameters, miss distances, and atmospheric fireball coordinates. It processes zero user data and keeps your vector store isolated from external telemetry.

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