How to Use the ButterCMS MCP in LangChain

Q: How do I pass ButterCMS tools to a LangChain agent?

Install langchain-mcp-adapters and use MultiServerMCPClient to connect to the ButterCMS endpoint. Call client.gettools() to retrieve tools like listblogposts and pass them directly to your agent constructor.

Q: Can LangChain run multi-step searches across ButterCMS?

Yes. Your LangChain agent can call searchblogposts to find specific articles, then use the resulting slug with getpostdetails in the next link of the chain to retrieve the full body text.

Build multi-step LangChain pipelines that query ButterCMS metadata, grab blog posts, and trace every tool call in LangSmith.

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Connect ButterCMS MCP to LangChain

Create your Vinkius account to connect ButterCMS 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 ButterCMS with LangChain

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Map ButterCMS page layouts in LangChain chains

Your LangChain agent can call `get_page_layout` to grab the exact structural blueprint of a page. This lets the chain inspect page configurations before deciding which content block to update or fetch next. Because LangChain chains tools together, the output of `get_page_layout` feeds directly into subsequent steps. LangSmith traces the exact layout payload so you can debug your CMS routing logic instantly.

Trace ButterCMS blog searches with this MCP Server

Stop guessing how your agent finds content. Use `search_blog_posts` inside your LangChain agent to run keyword searches across your entire ButterCMS blog library. The MCP Server exposes your blog taxonomy directly to the LLM. It can cross-reference results using `list_butter_categories` to filter out irrelevant posts before passing the final list to your generation step.

Extract global collections for dynamic routing

Let your LangChain agent fetch raw structured data using `list_global_collections` to build navigation menus or landing pages dynamically. The agent analyzes the collection schemas on the fly to route users to the right content. By combining `list_custom_pages` with collection data, your chain handles complex routing logic without hardcoded endpoints. Every single tool call shows up in your LangSmith dashboard for easy performance monitoring.

Setup guide

Set up ButterCMS 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 ButterCMS 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({
    "buttercms-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 ButterCMS 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 ButterCMS. 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 ButterCMS MCP in LangChain

Install `langchain-mcp-adapters` and use `MultiServerMCPClient` to connect to the ButterCMS endpoint. Call `client.get_tools()` to retrieve tools like `list_blog_posts` and pass them directly to your agent constructor.

Yes. Your LangChain agent can call `search_blog_posts` to find specific articles, then use the resulting slug with `get_post_details` in the next link of the chain to retrieve the full body text.

LangSmith captures the exact inputs and outputs for every ButterCMS tool call made by your LangChain agent. You can see the raw JSON returned by `list_butter_tags` or `list_butter_authors` to pinpoint where a chain failed.

By default, the LangChain MCP client is stateless. If you need to maintain context across multiple ButterCMS calls, use `client.session()` to run your tools within a persistent session.

Vinkius manages your ButterCMS API keys inside a secure, zero-trust V8 Isolate sandbox. Your LangChain code only interacts with the local MCP endpoint, meaning raw credentials never leak into your application logs or prompt histories.

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