How to Use the NASA Mars — Rover Photos from the Red Planet MCP in CrewAI

Q: How do I configure the NASA Mars — Rover Photos from the Red Planet MCP Server in CrewAI?

Run pip install crewai "crewaitools". Pass the server URL directly into your Agent's mcps list. The framework instantly exposes all four rover tools to that specific agent.

Q: Can I restrict the NASA Mars — Rover Photos from the Red Planet MCP Server to a single agent in CrewAI?

Yes. Use MCPServerHTTP from crewai.mcp and apply a toolfilter. You can give the getmarsmanifest tool to your planner agent while restricting the actual photo tools to your workers.

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Connect NASA Mars — Rover Photos from the Red Planet MCP to CrewAI

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Setup NASA Mars — Rover Photos from the Red Planet with CrewAI

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Assign agents to monitor active rovers

The `get_mars_latest` tool allows a dedicated CrewAI monitoring agent to watch Curiosity's daily uploads autonomously. You assign one agent the specific role of checking for new surface data. It runs continuously, pulling the newest telemetry without human intervention. When the monitoring agent detects new shots, it passes the data to an analyst agent in your crew. This shared memory model means the second agent immediately knows the exact sol and camera details without having to re-fetch the data.

Research historical archives autonomously

The `get_mars_manifest` tool gives your researcher agents the context they need to plan massive data pulls. A planning agent queries the manifest to map out the exact active dates for Spirit and Opportunity. It then delegates the actual downloading to worker agents. This hierarchical execution prevents API waste. The planner knows exactly which sols exist, so the workers never hit dead endpoints. They just execute `get_mars_photos` precisely where the data lives.

Filter by camera using the CrewAI MCP Server

The `get_mars_photos_by_date` tool lets your agents cross-reference Earth dates with specific camera hardware. You instruct a specialist agent to find only CHEMCAM or MAHLI images from a specific month in 2015. The agent handles the date conversion and the API routing. You pass the server URL directly into your `mcps` array. The framework automatically exposes these specific targeting tools to the agents you select, keeping your crew focused on exact mission parameters.

Setup guide

Set up NASA Mars — Rover Photos from the Red Planet 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 Mars — Rover Photos from the Red Planet tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="NASA Mars — Rover Photos from the Red Planet Analyst",
    goal="Access and analyze NASA Mars — Rover Photos from the Red Planet data via MCP.",
    backstory="Expert analyst with direct NASA Mars — Rover Photos from the Red Planet access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent NASA Mars — Rover Photos from the Red Planet 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 Mars — Rover Photos from the Red Planet Analyst",
        goal="Access and analyze NASA Mars — Rover Photos from the Red Planet data via MCP.",
        backstory="Expert analyst with direct NASA Mars — Rover Photos from the Red Planet access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent NASA Mars — Rover Photos from the Red Planet 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 Mars — Rover Photos from the Red Planet MCP in CrewAI

Run `pip install crewai "crewai[tools]"`. Pass the server URL directly into your Agent's `mcps` list. The framework instantly exposes all four rover tools to that specific agent.

Yes. Use `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. You can give the `get_mars_manifest` tool to your planner agent while restricting the actual photo tools to your workers.

You define the camera types in your agent's role description. The agent reads the available arguments for `get_mars_photos` and autonomously selects FHAZ, RHAZ, or MAST based on your prompt.

It works perfectly with sequential pipelines. You build a sequence where Task 1 finds the active dates using the manifest, Task 2 fetches the images, and Task 3 writes a report.

The integration is entirely zero-trust. Your agents pull raw payload URLs and mission manifests from an ephemeral V8 isolate. No proprietary crew memory or agent logic ever leaves your local Python environment.

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