How to Use the NASA Full — Ultimate Space Intelligence MCP in CrewAI

Q: How do I assign NASA Full — Ultimate Space Intelligence to specific CrewAI agents?

Use the toolfilter parameter when instantiating MCPServerHTTP. This restricts an agent to only the endpoints it actually needs, preventing context bloat.

Q: How do I configure NASA Full — Ultimate Space Intelligence inside my CrewAI Python script?

Install the crewaitools package. Pass the remote URL directly into the mcps array within your agent definition.

Deploy autonomous CrewAI teams to monitor solar flares, track near-Earth asteroids, and analyze exoplanet data.

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Connect NASA Full — Ultimate Space Intelligence MCP to CrewAI

Create your Vinkius account to connect NASA Full — Ultimate Space Intelligence to CrewAI 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 Full — Ultimate Space Intelligence with CrewAI

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Track Hazardous Asteroids

`get_neo_feed` and `get_neo_lookup` allow a dedicated watcher agent to track potentially hazardous asteroids. You assign these specific tools to a single crew member focused entirely on planetary defense. It polls the feed, identifies anomalies, and passes the SPK-IDs to an analyst agent for deep review. `get_close_approaches` provides the exact velocity and miss-distance metrics needed for that analysis. This MCP Server handles the complex schema mapping behind the scenes. The crew works sequentially, turning raw telemetry into a formatted threat assessment without human intervention.

Automate Mars Research

`get_mars_photos_by_date` and `get_mars_manifest` equip a research agent to scrape the Curiosity rover archives. The agent checks the manifest for active sol dates before attempting to pull images. This prevents wasted API calls and keeps the crew operating efficiently within rate limits. `get_media_asset` lets a secondary agent download the high-resolution files based on the primary agent's findings. CrewAI's shared memory ensures the NASA IDs transfer perfectly between roles. You get a fully automated pipeline for cataloging Martian terrain.

Monitor Solar Radiation

`get_cme` and `get_solar_energetic_particles` feed critical solar weather data to a monitoring crew. Coronal mass ejections dictate satellite safety. A supervisor agent evaluates the particle density and decides if it needs to trigger a simulated emergency response protocol. `get_radiation_belt` measures the impact of those CMEs on Earth's magnetic field. You configure the HTTP transport in your Python script using `MCPServerHTTP`. The framework restricts tool access based on your definitions, keeping agents focused on their specific domains.

Setup guide

Set up NASA Full — Ultimate Space Intelligence MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke NASA Full — Ultimate Space Intelligence tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="NASA Full — Ultimate Space Intelligence Analyst",
    goal="Access and analyze NASA Full — Ultimate Space Intelligence data via MCP.",
    backstory="Expert analyst with direct NASA Full — Ultimate Space Intelligence access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent NASA Full — Ultimate Space Intelligence transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="NASA Full — Ultimate Space Intelligence Analyst",
        goal="Access and analyze NASA Full — Ultimate Space Intelligence data via MCP.",
        backstory="Expert analyst with direct NASA Full — Ultimate Space Intelligence access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent NASA Full — Ultimate Space Intelligence transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 Full — Ultimate Space Intelligence MCP in CrewAI

Use the `tool_filter` parameter when instantiating `MCPServerHTTP`. This restricts an agent to only the endpoints it actually needs, preventing context bloat.

That is exactly how the framework operates. The first agent pulls the rover data and writes it to shared memory. The second agent reads that context and executes the analysis.

Install the `crewai[tools]` package. Pass the remote URL directly into the `mcps` array within your agent definition.

Yes. A manager agent can evaluate the available 32 tools and delegate specific queries like `query_confirmed_planets` to subordinate researchers.

Exoplanet filter parameters exist strictly within the zero-trust execution boundary. The Vinkius infrastructure routes the request to the NASA archive and destroys the container state after transmission, leaving no trace of your query parameters.

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