How to Use the MasterGo MCP in CrewAI

Q: How do I add the MasterGo MCP to CrewAI?

Pass the MasterGo endpoint URL directly into the mcps array when defining your CrewAI agent. The agent automatically discovers and registers all ten available design tools.

Q: How do I restrict which MasterGo MCP tools a CrewAI agent can use?

Import MCPServerHTTP from crewai.mcp and use the toolfilter parameter. You can restrict a junior agent to only use getcomments while giving the manager full access.

Q: Can CrewAI automatically summarize MasterGo project files?

Set up a sequential task pipeline. Task one uses getprojectfiles to gather the targets. Task two reads the contents. Task three writes the summary. The crew handles the handoffs natively.

Deploy autonomous AI crews to audit design systems and monitor MasterGo via MCP Server using CrewAI.

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Connect MasterGo MCP to CrewAI

Create your Vinkius account to connect MasterGo 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 MasterGo with CrewAI

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Coordinate Specialized Design Crews

`list_projects` feeds your CrewAI manager agent a complete map of your team's active work. The manager delegates specific projects to worker agents based on their assigned roles. One agent takes the UX research role, calling `get_comments` to analyze user feedback. Another agent acts as the design system engineer, running `list_styles` to check for token consistency. They share memory and build a unified report.

MasterGo MCP Server for Autonomous Audits

`list_files` retrieves every document within a workspace. Your CrewAI monitor agent watches this list continuously. When a new file appears, the monitor triggers the analysis pipeline. The pipeline executes sequentially. First, it grabs the metadata. Then it calls `list_nodes` to inspect the layer hierarchy. Finally, a specialized reporting agent writes a summary of the new design and posts it to your engineering channel.

Map Organizational Structures

`list_teams` and `get_org_members` provide a complete view of who owns what in your design organization. A CrewAI administrative agent uses this data to map reporting lines and project ownership. This happens without human oversight. The agent pulls the data, identifies orphaned projects where the original creator left the company, and flags them for reassignment. You get clean workspace management automatically.

Setup guide

Set up MasterGo 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 MasterGo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the MasterGo endpoint URL directly into the `mcps` array when defining your CrewAI agent. The agent automatically discovers and registers all ten available design tools.

Yes. Your crew shares the connection. A research agent can call `get_file` while a parallel compliance agent runs `get_org_members` to verify access rights.

Import `MCPServerHTTP` from `crewai.mcp` and use the `tool_filter` parameter. You can restrict a junior agent to only use `get_comments` while giving the manager full access.

Set up a sequential task pipeline. Task one uses `get_project_files` to gather the targets. Task two reads the contents. Task three writes the summary. The crew handles the handoffs natively.

The server processes design tokens, comments, and version histories strictly in memory. We enforce zero-trust isolation on every request. Authentication relies on a single endpoint token, and no client data is stored on disk.

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