How to Use the Frontify MCP in AutoGen

Q: How do I register Frontify tools in an AutoGen agent?

Use mcpservertools with your server URL to retrieve the tools from this MCP Server. Pass the resulting list to your AssistantAgent constructor to give it access to tools like listnativebrands.

Q: How do AutoGen agents handle Frontify workspace limits?

Your agents can call getaccountlimits to check current boundaries. If limits are close to being exceeded, the agents can debate whether to archive old projects using listworkspaceprojects.

Build multi-agent debate loops to manage Frontify brand assets inside AutoGen with this MCP Server.

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Connect Frontify MCP to AutoGen

Create your Vinkius account to connect Frontify to AutoGen 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 AutoGen

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Run consensus-driven asset purging in AutoGen

The `wipe_media_asset` tool requires careful execution, making it perfect for AutoGen multi-agent debate loops. You can set up a design agent that requests a deletion, while a compliance agent checks `list_brand_guidelines` before agreeing to the purge. This debate ensures that no active brand assets are deleted by accident. The agents must reach a consensus before the tool is allowed to run, protecting your production asset library from rogue deletions.

Validate user access through agent debate

The `invite_workspace_user` tool can be gated behind an automated approval process in your AutoGen workflows. A security agent checks `list_platform_users` to verify the email domain, while an admin agent confirms workspace capacity using `get_account_limits`. Only when both agents agree does the system send the invitation. This automated vetting prevents unauthorized users from gaining access to sensitive brand assets.

Coordinate metadata updates across workspaces

The `patch_asset_metadata` tool allows your agents to standardize tags across multiple workspaces. A coordinator agent lists projects via `list_workspace_projects`, while a worker agent updates the metadata attributes for each asset found. This multi-agent coordination makes bulk updates fast and reliable. You don't have to write complex loops; the agents divide the work and execute the changes in parallel.

Setup guide

Set up Frontify MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Frontify tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Frontify_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Frontify data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Frontify_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Frontify data")
    print(result.messages[-1].content)

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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 AutoGen

Use mcp_server_tools with your server URL to retrieve the tools from this MCP Server. Pass the resulting list to your AssistantAgent constructor to give it access to tools like list_native_brands.

Yes. You can configure a security agent to inspect the payload for execute_graphql_payload before a database agent is allowed to execute it.

Your agents can call get_account_limits to check current boundaries. If limits are close to being exceeded, the agents can debate whether to archive old projects using list_workspace_projects.

Yes, this server works with both transports. You can configure StreamableHttpServerParams in AutoGen to connect to your hosted instance.

This setup manages user directories, brand assets, and workspace metadata. All operations run in an isolated V8 MCP sandbox, ensuring your API credentials and brand data are never exposed or stored.

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