Chattermill MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Chattermill as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Chattermill. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Chattermill?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Chattermill tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Chattermill MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 11 tools from Chattermill
Why Use LlamaIndex with the Chattermill MCP Server
LlamaIndex provides unique advantages when paired with Chattermill through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Chattermill tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Chattermill tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Chattermill, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Chattermill tools were called, what data was returned, and how it influenced the final answer
Chattermill + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Chattermill MCP Server delivers measurable value.
Hybrid search: combine Chattermill real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Chattermill to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Chattermill for fresh data
Analytical workflows: chain Chattermill queries with LlamaIndex's data connectors to build multi-source analytical reports
Chattermill MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Chattermill to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Chattermill to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpChattermill + LlamaIndex FAQ
Common questions about integrating Chattermill MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Chattermill with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Chattermill to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.