How to Use the Atlas MCP in CrewAI

Deploy autonomous support crews that monitor Atlas queues, research articles, and resolve tickets using CrewAI.

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Connect Atlas MCP to CrewAI

Create your Vinkius account to connect Atlas 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 Atlas with CrewAI

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Claude

Perplexity

Multi-agent ticket resolution

One script trying to do everything usually fails. You can assign the `list_tickets` tool to a dedicated monitoring agent that only watches the queue for new entries. When a high-priority issue hits, that monitor alerts a researcher agent. The researcher scans `list_articles` for known fixes and hands the solution to a responder to draft the reply.

Atlas MCP Server integration

Python developers need reliable connections to external APIs without writing boilerplate. Passing the Vinkius URL straight into the mcps array wires up the entire suite of tools instantly. Your crew gains immediate read and write access to the support platform. They can pull agent lists via `list_users` or verify connection status with `get_account_check` before starting a complex task.

Filtered tool access

Giving every agent full write permissions is asking for trouble. CrewAI lets you filter this MCP Server's tools so you restrict exactly what each role can touch. You hand the `get_ticket` read-only operation to the junior analyst bot. The senior moderator agent gets the `create_ticket` capability so only approved logic actually modifies the database.

Setup guide

Set up Atlas 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 Atlas tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Atlas 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="Atlas Analyst",
        goal="Access and analyze Atlas data via MCP.",
        backstory="Expert analyst with direct Atlas access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Atlas 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 Atlas. 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 Atlas MCP in CrewAI

The simplest method is adding the Vinkius endpoint directly to the mcps parameter in your Agent definition. The framework automatically parses and registers the available tools.

Shared memory is built into the framework. When your researcher pulls details using `get_customer`, the responder agent can read that exact context later in the execution chain.

Import MCPServerHTTP from the crewai.mcp module. Use the tool_filter argument to expose specific operations to specific agents based on their assigned roles.

You can use SSE, standard I/O, or Streamable HTTP. The client handles the underlying transport mechanics so your Python code stays focused on the logic.

Authentication happens at the Vinkius perimeter. Your private support logs, agent names from `list_users`, and internal tickets remain isolated in a single-use execution context that vanishes after completion.

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