How do I find the project key I need to use with most tools?

Start by asking the agent to run `list_chattermill_projects`. This returns all your projects with their keys. Most Chattermill API endpoints — responses, themes, metrics, etc. — require this project key as the first parameter.

What customer experience metrics can I retrieve?

Use `get_chattermill_metric` with a metric type of `nps`, `average_score`, `net_sentiment`, or `volume`. You can filter by date range, category, or theme for more targeted insights. All metrics require a project key.

Where do I find my Chattermill API Key?

Log in to your Chattermill account and navigate to **Settings > API**. You can generate and copy your personal API Key from that section. The key is used as a Bearer token on every API request.

Chattermill MCP Server for LangChain 11 tools — connect in under 2 minutes

Name: Chattermill
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 11 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Chattermill through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "chattermill": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Chattermill, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Chattermill MCP Server

Connect your Chattermill account to any AI agent and take full control of your customer experience (CX) intelligence through natural conversation. Unify feedback from Zendesk, App Store, Typeform, and dozens of other sources into one AI-powered view.

LangChain's ecosystem of 500+ components combines seamlessly with Chattermill through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Project Management — List and inspect all feedback projects configured in your account
Feedback Intelligence — Browse, filter, and paginate customer responses with full date and source filtering
Theme Analysis — Explore AI-generated themes and categories to pinpoint recurring customer issues
Metric Insights — Retrieve calculated NPS, CSAT, net sentiment, and volume metrics on demand
Source Auditing — List all data sources and data types feeding your feedback pipeline
Segmentation — Access custom segments for advanced cohort analysis
Data Ingestion — Submit new feedback entries for analysis directly from your agent

The Chattermill MCP Server exposes 11 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Chattermill to LangChain via MCP

Follow these steps to integrate the Chattermill MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 11 tools from Chattermill via MCP

Why Use LangChain with the Chattermill MCP Server

LangChain provides unique advantages when paired with Chattermill through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Chattermill MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Chattermill queries for multi-turn workflows

Chattermill + LangChain Use Cases

Practical scenarios where LangChain combined with the Chattermill MCP Server delivers measurable value.

RAG with live data: combine Chattermill tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Chattermill, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Chattermill tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Chattermill tool call, measure latency, and optimize your agent's performance

Chattermill MCP Tools for LangChain (11)

These 11 tools become available when you connect Chattermill to LangChain via MCP:

get_chattermill_metric

Valid metric_type values: nps, average_score, net_sentiment, volume. Supports optional date range filtering with UNIX timestamps. Retrieve a calculated metric (NPS, CSAT, sentiment, volume) for a project

get_chattermill_project

Use list_chattermill_projects first if the project ID is unknown. Get details of a specific Chattermill project by its ID

get_response_details

Returns the comment, score, metadata, and applied themes. Get detailed information for a single feedback response

list_chattermill_projects

Use this first to obtain the project key needed by all other Chattermill tools. The project key is typically a lowercase version of the company name. List all available feedback projects in the Chattermill account

list_custom_segments

Returns user-defined segments used for advanced filtering and cohort analysis. List custom segments defined for a project

list_data_types

Returns data classification types used to categorize responses. Use this to discover type keys for filtering. List all feedback data types for a project (e.g. NPS, review, survey)

list_feedback_responses

Supports pagination via page/per_page and date filtering via date_from/date_to in YYYYMMDD_HHMMSS format. Default: page 1, 20 results per page, max 100. List paginated feedback responses for a specific project

list_feedback_sources

Returns configured data ingestion sources. Use this to discover available source keys for filtering responses. List all feedback data sources for a project (e.g. Zendesk, App Store, Typeform)

list_feedback_themes

Returns themes automatically generated by Chattermill ML to classify recurring customer topics. List AI-generated feedback themes detected in a project

list_theme_categories

Categories are parent groupings for themes, useful for high-level trend analysis. List categories that group feedback themes together

submit_feedback_response

Requires the project_key plus comment text. Optionally supply score, data_source, and data_type keys from their respective list endpoints. Submit a new feedback response to a Chattermill project

Example Prompts for Chattermill in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Chattermill immediately.

"List all my Chattermill projects and then show me the latest feedback responses from the first one."

"What is our current NPS score for the 'acme' project?"

"Show me the AI-detected themes and their categories for my mobile app project."

Troubleshooting Chattermill MCP Server with LangChain

Common issues when connecting Chattermill to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Chattermill + LangChain FAQ

Common questions about integrating Chattermill MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.