How to Use the Chattermill MCP in LlamaIndex

Q: Can LlamaIndex query historical NPS metrics from Chattermill?

Yes. Your query engine uses getchattermillmetric with UNIX timestamps to fetch historical NPS or sentiment data for any date range.

Q: What is the best way to filter feedback before indexing?

Have your agent call listdatatypes to discover available classifications first. Use those keys to restrict the responses you pull into your vector store.

Ground your LlamaIndex RAG pipelines with live customer sentiment and feedback themes from Chattermill.

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Connect Chattermill MCP to LlamaIndex

Create your Vinkius account to connect Chattermill to LlamaIndex 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 LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index live customer feedback into vector stores

Feeding raw user opinions into your index is now fully automated with the `list_feedback_responses` tool. Instead of relying on static documents, your agent gets real-time context. This prevents hallucinations when querying your knowledge base about current customer satisfaction trends.

Ground query engines with Chattermill MCP Server data

Synthesizing answers about customer pain points requires fetching metrics with the `get_chattermill_metric` tool. By using `list_custom_segments`, the query engine narrows down search results to specific user cohorts. This ensures your generated summaries reflect the exact segment you asked about.

Identify and filter feedback by AI-generated themes

Raw text search often misses the core issue, but your agent can run the `list_feedback_themes` tool to retrieve structured categories. Combine these themes with `list_data_types` to filter your index by specific feedback channels like mobile reviews or support surveys. Your search queries become highly targeted as a result.

Setup guide

Set up Chattermill MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Chattermill MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Chattermill tools.",
)
response = await agent.run("List recent Chattermill data")

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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 Chattermill. 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 Chattermill MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the basic client. Wrap the connection in McpToolSpec and convert it to a tool list to use with your query engines.

Yes. Your query engine uses get_chattermill_metric with UNIX timestamps to fetch historical NPS or sentiment data for any date range.

It replaces outdated spreadsheets with live API data. Your RAG app queries real comments retrieved by list_feedback_responses instead of stale files.

Have your agent call list_data_types to discover available classifications first. Use those keys to restrict the responses you pull into your vector store.

Your data is protected by default. Customer comments, NPS scores, and sentiment metrics flow through an ephemeral V8 sandbox. Vinkius never caches or inspects the feedback records processed by the MCP Server.

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