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

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

Install autogen-extmcp. Initialize mcpservertools with a StreamableHttpServerParams pointing to the server. Pass the resulting tool list directly to your AssistantAgent.

Q: Can AutoGen agents handle the pagination in getneobrowse?

Yes. An agent will call getneobrowse, read the first 20 records, and decide if it needs to call the tool again for the next page based on its current instructions.

Let AutoGen agents debate planetary defense strategies using live NASA asteroid data.

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

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

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Perplexity

Debate threat levels of upcoming encounters.

`get_close_approaches` and `get_neo_feed` supply the raw trajectory and hazard data your AutoGen agents need to debate threat levels. You assign one agent to act as a risk assessor pulling upcoming encounters, while a second agent acts as a skeptic verifying the miss distance and diameter. The agents negotiate the actual severity of the event. If the first agent flags a sub-140m rock passing within 0.01 AU, the skeptic might argue that its small size makes it a non-threat. They reach a consensus before alerting you.

Calibrate risk models with historical impacts.

`get_fireballs` returns exact atmospheric impact events—including kiloton energy equivalents and velocities—detected by government sensors. Your multi-agent system can use this to calibrate its risk models. One agent fetches historical bolide data, while another compares those energy yields against the estimated diameters from current near-Earth objects. You build a system that argues over kinetic energy calculations instead of just spitting out a raw data feed.

Divide and conquer catalog research.

`get_neo_lookup` allows an agent to pull deep orbital specifics on an SPK-ID while another agent pages through the broader catalog using `get_neo_browse`. They work in tandem. The browsing agent identifies anomalies in the known catalog and hands the IDs off to the lookup agent for detailed profiling. Microsoft's framework handles the coordination. You just watch the logs as they divide the work and compile a joint report.

Setup guide

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

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes NASA Asteroids — Near-Earth Objects & Planetary Defense tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="NASA Asteroids — Near-Earth Objects & Planetary Defense_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent NASA Asteroids — Near-Earth Objects & Planetary Defense data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="NASA Asteroids — Near-Earth Objects & Planetary Defense_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent NASA Asteroids — Near-Earth Objects & Planetary Defense data")
    print(result.messages[-1].content)

Get your connection token →

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

Install `autogen-ext[mcp]`. Initialize `mcp_server_tools` with a `StreamableHttpServerParams` pointing to the server. Pass the resulting tool list directly to your `AssistantAgent`.

Create a dedicated Data Retrieval Agent that holds the tools. Other agents in the chat will request data from it, sparking a conversation about the results.

Yes. An agent will call `get_neo_browse`, read the first 20 records, and decide if it needs to call the tool again for the next page based on its current instructions.

Write it into the system prompt of your analysis agent. Instruct it to always cross-reference new asteroid threats against past atmospheric impacts using the fireball tool.

The server strictly handles public space domain awareness data, fetching orbital velocities, SPK-IDs, and fireball coordinates. It reads no local files and exposes zero user chat history to external APIs.

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