Chattermill MCP Server for OpenAI Agents SDK 11 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Chattermill through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Chattermill Assistant",
instructions=(
"You help users interact with Chattermill. "
"You have access to 11 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Chattermill"
)
print(result.final_output)
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.
The OpenAI Agents SDK auto-discovers all 11 tools from Chattermill through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Chattermill, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Chattermill MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 11 tools from Chattermill
Why Use OpenAI Agents SDK with the Chattermill MCP Server
OpenAI Agents SDK provides unique advantages when paired with Chattermill through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Chattermill + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Chattermill MCP Server delivers measurable value.
Automated workflows: build agents that query Chattermill, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Chattermill, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Chattermill tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Chattermill to resolve tickets, look up records, and update statuses without human intervention
Chattermill MCP Tools for OpenAI Agents SDK (11)
These 11 tools become available when you connect Chattermill to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Chattermill to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Chattermill + OpenAI Agents SDK FAQ
Common questions about integrating Chattermill MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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 OpenAI Agents SDK
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.