How to Use the Chattermill MCP in LangChain

Q: Can a LangChain agent query specific feedback sources in Chattermill?

Yes. Your agent can run listfeedbacksources to find the exact channel keys, then filter the feedback list to isolate specific pipelines.

Q: What happens if my agent does not know the project key?

Have your agent call listchattermillprojects first. It returns all active projects under your account, allowing the agent to dynamically choose the correct key.

Build LangChain agents that pull real-time sentiment metrics and customer feedback trends directly into your chains.

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Connect Chattermill MCP to LangChain

Create your Vinkius account to connect Chattermill 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 Chattermill with LangChain

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Track customer sentiment metrics inside LangChain chains

Your LangChain agent can fetch live NPS, sentiment, and volume data using the `get_chattermill_metric` tool. This isn't static data; your chains pull fresh numbers during execution to route tasks based on real-world customer mood. Use `get_chattermill_project` to isolate specific customer cohorts. By tracing these calls in LangSmith, you see exactly how feedback trends influence your agent's decision path.

Analyze feedback themes with multi-step ReAct agents

Discovering why users are complaining requires calling the `list_feedback_themes` tool to isolate recurring topics automatically categorized by customer support teams. Once the agent detects a pattern, it can pull category details via `list_theme_categories` to group issues. This lets your ReAct loop decide whether to trigger an alert or draft a response based on category severity.

Submit feedback directly from your LangChain pipeline

Processing raw support tickets from databases or external APIs is simple with the `submit_feedback_response` tool. Before sending, the agent runs `list_feedback_sources` to map the incoming record to the correct channel. This keeps your central dashboard clean and accurately categorized without manual intervention.

Setup guide

Set up Chattermill 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 Chattermill 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({
    "chattermill-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 Chattermill transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Chattermill MCP in LangChain

Install langchain-mcp-adapters via pip. Initialize the MultiServerMCPClient with your Vinkius endpoint URL, and pass the retrieved tools directly into your agent's tool list.

Yes. Your agent can run list_feedback_sources to find the exact channel keys, then filter the feedback list to isolate specific pipelines.

LangSmith tracks the exact payload, latency, and output of tools like get_chattermill_metric. You can inspect the raw JSON data flowing through your chains in real time.

Have your agent call list_chattermill_projects first. It returns all active projects under your account, allowing the agent to dynamically choose the correct key.

Vinkius runs the server in an isolated V8 sandbox, meaning your customer comments and NPS scores are never stored on our platform. All data passes directly between your LangChain environment and the target API.

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