How to Use the Frontify MCP in CrewAI

Q: Does CrewAI support complex Frontify GraphQL queries?

It does. You pass your query strings to executegraphqlpayload. Your research agent can then analyze the output and direct the action agents accordingly.

Deploy specialized agent crews to manage Frontify brand assets and project lifecycle.

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Connect Frontify MCP to CrewAI

Create your Vinkius account to connect Frontify 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 Frontify with CrewAI

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Agent-led Frontify asset management

Assign a dedicated agent to monitor your projects using `get_project_assets`. A separate agent can then perform `patch_asset_metadata` based on the findings. Your crew works in tandem. One agent identifies the work, and the other executes the changes, reducing the burden on your design team.

Autonomous Frontify brand monitoring

Deploy a monitor agent to audit your portal with `list_brand_guidelines`. It flags discrepancies and passes them to a moderator agent for resolution. Scale your oversight without manual intervention. The agents follow your hierarchy to ensure brand consistency across all your active global namespaces.

Direct Frontify platform coordination

Use `list_platform_users` and `list_workspace_projects` to manage your team access. Your crew ensures that only the correct users have access to specific workspaces. Keep your portals locked down. The agents verify user roles and update permissions automatically whenever a project status changes in Frontify.

Setup guide

Set up Frontify 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 Frontify tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Absolutely. You can assign different agents to research brand guidelines and update assets. This gives you a scalable way to maintain large brand portals.

You use the tool filter in your agent configuration to limit which tools each member of the crew can access. This keeps your asset deletion or user management tools restricted to authorized agents.

It does. You pass your query strings to `execute_graphql_payload`. Your research agent can then analyze the output and direct the action agents accordingly.

CrewAI uses shared memory for your team. When one agent runs `list_workspace_projects`, the results are available to the rest of the crew for their specific tasks.

Your data stays protected by Vinkius's ephemeral runtime. Only the specific asset IDs and metadata required for the current task are exposed to your agent crew.

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