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

Q: How do I connect NASA Asteroids — Near-Earth Objects & Planetary Defense to LangChain?

Install langchain-mcp-adapters and langgraph. Initialize a MultiServerMCPClient pointing to the server URL, call client.gettools(), and pass the array to your agent constructor.

Q: Can my LangChain agent automatically track hazardous asteroids using NASA Asteroids — Near-Earth Objects & Planetary Defense?

Yes. You can build a cron-triggered chain that calls getneofeed to check for new objects. If the hazard flag is true, the agent extracts the data and executes your custom alert logic.

Q: Does NASA Asteroids — Near-Earth Objects & Planetary Defense work with LangSmith tracing?

It does. Every call to getneobrowse or getneolookup appears in your trace logs. You see the exact inputs the agent sent and the raw JSON it got back.

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

Create your Vinkius account to connect NASA Asteroids — Near-Earth Objects & Planetary Defense to LangChain 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 NASA Asteroids — Near-Earth Objects & Planetary Defense with LangChain

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Claude

Perplexity

Chain asteroid feeds into deep orbital profiles.

`get_neo_feed` grabs upcoming asteroids and feeds their IDs directly into `get_neo_lookup` for deep profiling. LangChain excels at this exact sequence. You build a chain where the agent pulls the weekly hazard list, extracts the SPK-IDs, and automatically queries the specific orbital parameters for each rock. LangSmith tracing lets you watch the agent iterate through the list. If an asteroid meets your custom hazard threshold, the chain can trigger an alert or write a report. You control the exact logic flow.

Automate close approach filtering.

`get_close_approaches` filters future Earth encounters by distance, date, and minimum size using JPL CNEOS data. Your ReAct agent can evaluate these encounters against historical baselines. You set the parameters—say, anything sub-0.05 AU and over 140 meters in diameter—and the agent decides if it warrants further investigation. Because LangChain supports multi-server aggregation, you can pipe this close approach data directly into another MCP server. The agent grabs the trajectory, formats it, and posts it to a database without you writing glue code.

Correlate fireball events with past predictions.

`get_fireballs` returns atmospheric impact events, including energy in kilotons of TNT equivalent, detected by US government sensors. You can build a chain that correlates these impacts with past near-Earth object predictions. The agent pulls the event location and altitude, then cross-references it with known blind spots in your catalog. It is a straightforward data pipeline. You get the raw impact metrics structured exactly how your downstream tools need them.

Setup guide

Set up NASA Asteroids — Near-Earth Objects & Planetary Defense MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes NASA Asteroids — Near-Earth Objects & Planetary Defense tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "nasa-asteroids-near-earth-objects-planetary-defense-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent NASA Asteroids — Near-Earth Objects & Planetary Defense transactions"
    })
    print(result["messages"][-1].content)

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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 NASA. 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 NASA Asteroids — Near-Earth Objects & Planetary Defense MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph`. Initialize a `MultiServerMCPClient` pointing to the server URL, call `client.get_tools()`, and pass the array to your agent constructor.

Yes. You can build a cron-triggered chain that calls `get_neo_feed` to check for new objects. If the hazard flag is true, the agent extracts the data and executes your custom alert logic.

The tool returns structured JSON containing atmospheric impact metrics. Your chain receives exact velocity, altitude, and energy yields in kilotons, ready for parsing.

It does. Every call to `get_neo_browse` or `get_neo_lookup` appears in your trace logs. You see the exact inputs the agent sent and the raw JSON it got back.

This server only reads public astronomical data like asteroid SPK-IDs, orbital velocities, and fireball energy yields. It requires zero user authentication and transmits no personal information to NASA or JPL.

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