Chattermill MCP Server for Mastra AI 11 tools — connect in under 2 minutes
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect Chattermill through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token. get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"chattermill": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "Chattermill Agent",
instructions:
"You help users interact with Chattermill " +
"using 11 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with Chattermill?"
);
console.log(result.text);
}
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.
Mastra's agent abstraction provides a clean separation between LLM logic and Chattermill tool infrastructure. Connect 11 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.
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 Mastra AI 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 Mastra AI via MCP
Follow these steps to integrate the Chattermill MCP Server with Mastra AI.
Install dependencies
Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.ts and run with npx tsx agent.ts
Explore tools
Mastra discovers 11 tools from Chattermill via MCP
Why Use Mastra AI with the Chattermill MCP Server
Mastra AI provides unique advantages when paired with Chattermill through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add Chattermill without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every Chattermill tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure
Chattermill + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the Chattermill MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query Chattermill, process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed Chattermill as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query Chattermill on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using Chattermill tools alongside other MCP servers
Chattermill MCP Tools for Mastra AI (11)
These 11 tools become available when you connect Chattermill to Mastra AI 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 Mastra AI
Ready-to-use prompts you can give your Mastra AI 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 Mastra AI
Common issues when connecting Chattermill to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpChattermill + Mastra AI FAQ
Common questions about integrating Chattermill MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
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 Mastra AI
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.