How to Use the KeepTrack Space Intelligence MCP in CrewAI

Q: How do I connect KeepTrack Space Intelligence to CrewAI?

Pass the server URL directly into the mcps array when defining your agent. The framework handles the MCP Server connection and exposes the tools to that specific role.

Deploy autonomous aerospace tracking teams with CrewAI.

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Connect KeepTrack Space Intelligence MCP to CrewAI

Create your Vinkius account to connect KeepTrack 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 KeepTrack Space Intelligence with CrewAI

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Perplexity

Assign space launch tracking to dedicated agents

The `get_recent_space_launches` tool gives your researcher agent direct access to the orbital manifest. While one agent pulls the launch data, an analyst agent reviews the payloads for specific military or commercial hardware. You do not have to write custom scrapers. CrewAI passes the tool output through shared memory, letting the entire team see what just went into orbit.

Run deep satellite investigations with an MCP Server

The `search_satellites` tool acts as the starting point for your autonomous orbital tracking crew. A monitor agent queries for specific hardware, finds the matching identifiers, and hands them off to the next role in the sequence. The receiving agent then uses those IDs to query further. Because CrewAI supports hierarchical execution, a manager agent can evaluate the search results and decide if the team needs to dig deeper into this MCP Server.

Extract precise orbital telemetry

The `get_satellite_details` tool supplies the raw physics data your agents need to make decisions. It returns altitude, inclination, and operational status directly into the agent's context window. A specialized engineering agent can take this telemetry and write a daily briefing on orbital decay. The whole process runs entirely in the background without human intervention.

Setup guide

Set up KeepTrack 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 KeepTrack Space Intelligence tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent KeepTrack 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 →

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="KeepTrack Space Intelligence Analyst",
        goal="Access and analyze KeepTrack Space Intelligence data via MCP.",
        backstory="Expert analyst with direct KeepTrack Space Intelligence access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent KeepTrack 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 KeepTrack. 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 KeepTrack Space Intelligence MCP in CrewAI

Pass the server URL directly into the `mcps` array when defining your agent. The framework handles the MCP Server connection and exposes the tools to that specific role.

Yes. You assign the server connection to individual agents, not the whole crew. The researcher gets the tracking tools, while the writer only gets the text output.

CrewAI uses shared memory. Once the first agent pulls the launch manifest, the context is available to the rest of the team for the remainder of the session.

The framework supports stdio, SSE, and Streamable HTTP. Use the `MCPServerHTTP` class from `crewai.mcp` for remote server connections.

The MCP Server only receives the explicit arguments passed to the tools, like a satellite ID. It never reads your agents' shared memory, system prompts, or internal reasoning logs.

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