How to Use the Frontify MCP in LangChain

Q: Can LangChain run multi-step Frontify migrations?

Yes. You can build a chain where the agent uses listworkspaceprojects to find target folders, retrieves assets with getprojectassets, and updates them using patchassetmetadata in a single run.

Q: How do I debug Frontify and LangChain integration issues?

LangSmith logs every payload sent to executegraphqlpayload and tracks latency for all tool calls. You can pinpoint exactly why an asset update failed or which agent step triggered a rate limit.

Run multi-step brand asset pipelines directly in LangChain using this Frontify MCP Server.

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Connect Frontify MCP to LangChain

Create your Vinkius account to connect Frontify to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain asset cleanup with instant rollback

The `wipe_media_asset` tool lets your agent permanently delete damaged or outdated assets directly from your LangChain chains. You can build a chain that checks asset usage, flags bad metadata with `patch_asset_metadata`, and purges the file if it violates brand standards. LangSmith traces every step of this deletion chain, showing you exactly when the tool ran and what it wiped. If a run fails, the chain halts before deleting downstream assets, giving you immediate observability into your brand portal operations.

Invite users in LangChain onboarding agents

The `invite_workspace_user` tool lets you add team members to specific workspaces as a step in your LangChain onboarding agents. Your agent reads a new hire form, grabs the correct workspace ID using `list_workspace_projects`, and sends the invitation automatically. This setup lets you combine database lookups and user creation in a single, observable sequence. You don't have to jump between systems or write custom API integrations to keep your team access synced.

Query brand guidelines in multi-agent loops

The `list_brand_guidelines` tool exposes documentation structures directly to your LangChain ReAct agents. Your agent can query these guidelines to verify if a marketing campaign matches current brand rules before running a creative review. Integrating this tool with other API chains ensures your agents make decisions based on real-time documentation. It removes the guesswork from automated brand reviews by putting the source of truth directly into the agent's context.

Setup guide

Set up Frontify MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Frontify tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "frontify-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Frontify transactions"
    })
    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 LangChain

Use the MultiServerMCPClient to connect to this MCP Server and call client.get_tools(). Pass those tools directly to your create_agent call to let your agent use actions like get_project_assets.

Yes. You can build a chain where the agent uses list_workspace_projects to find target folders, retrieves assets with get_project_assets, and updates them using patch_asset_metadata in a single run.

LangSmith logs every payload sent to execute_graphql_payload and tracks latency for all tool calls. You can pinpoint exactly why an asset update failed or which agent step triggered a rate limit.

By default, this MCP Server is stateless, which works perfectly for quick tasks like running get_account_limits. Use client.session() if your agent needs to maintain context across multiple steps.

This setup accesses your brand assets, metadata attributes, and user lists. All operations run inside an ephemeral V8 MCP sandbox, ensuring your credentials and asset data are never exposed to external servers.

Use it with your favorite AI tools

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