How to Use the Chattermill MCP in AutoGen

Q: How do I register the Chattermill MCP Server with AutoGen?

Install autogen-extmcp and use the streamable HTTP parameters. Pass the adapter-wrapped tools directly into your AssistantAgent constructor.

Q: How do AutoGen agents handle unknown project IDs in Chattermill?

One agent can run listchattermillprojects to find the correct project key, then pass that value to other agents in the conversation group.

Enable AutoGen agent networks to debate customer feedback trends and coordinate sentiment analysis.

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Connect Chattermill MCP to AutoGen

Create your Vinkius account to connect Chattermill to AutoGen 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 AutoGen

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Coordinate sentiment analysis across AutoGen agents

Let your agents run joint analysis on customer mood using the `get_chattermill_metric` tool. This multi-agent setup ensures that decisions aren't made in isolation. Your agents cross-reference metrics with qualitative feedback to provide a balanced summary.

Debate customer pain points using theme classification

Understanding user complaints requires looking at issues from multiple angles, which is why a dedicated support agent pulls themes using the `list_feedback_themes` tool. They use `list_theme_categories` to group these issues into parent buckets. This structured debate helps your network prioritize engineering fixes over simple support workarounds.

Verify and submit feedback with consensus-driven agents

Before logging a new customer response, your agents can validate details and call the `submit_feedback_response` tool. The validation agent calls `list_feedback_sources` to ensure the source key matches existing integrations. This prevents corrupt or misclassified data from entering your main feedback loop.

Setup guide

Set up Chattermill MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Chattermill tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Chattermill_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Chattermill data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Chattermill_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Chattermill data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Install autogen-ext[mcp] and use the streamable HTTP parameters. Pass the adapter-wrapped tools directly into your AssistantAgent constructor.

Yes. Since the MCP client is stateless, different agents can call get_chattermill_metric for various timeframes or metrics without conflict.

One agent can run list_chattermill_projects to find the correct project key, then pass that value to other agents in the conversation group.

The integration supports both stdio and streamable HTTP transports, allowing you to run your agents locally or in cloud environments.

All data transfers happen over an encrypted connection through an ephemeral sandbox. Your customer comments, satisfaction scores, and metadata are never retained by Vinkius.

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