How to Use the Chattermill MCP in CrewAI

Q: How do I configure CrewAI to use the Chattermill MCP Server?

Install crewaitools and pass your Vinkius HTTP URL directly into the agent's mcps array. This instantly exposes all 11 feedback tools to that specific agent.

Q: Can I limit which Chattermill MCP tools a CrewAI agent can access?

Yes, you can use MCPServerHTTP from crewai.mcp and apply a toolfilter. This lets you restrict a writer agent to submitfeedbackresponse while keeping read tools private.

Deploy a team of specialized agents to analyze and act on customer feedback with CrewAI and the Chattermill MCP Server.

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Connect Chattermill MCP to CrewAI

Create your Vinkius account to connect Chattermill to CrewAI 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 CrewAI

Ask AI about this MCP

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Perplexity

Orchestrate multi-agent feedback analysis in CrewAI

The `list_feedback_responses` tool allows your research agent to gather the latest customer comments for analysis. Once gathered, a separate moderator agent can review the text and take action based on the content. This division of labor keeps your operations clean. While the research agent pulls the batch of responses via the MCP endpoint, the analyst agent uses `get_response_details` to dissect the specific themes and metadata associated with each comment.

Monitor team performance using Chattermill metrics

The `get_chattermill_metric` tool provides your supervisor agent with high-level NPS and CSAT scores to track support quality. Our supervisor agent compares these metrics over time to evaluate if your customer service crew is meeting its targets. By running this check on a schedule, your autonomous crew spots performance dips immediately. To do this, the supervisor agent queries `list_chattermill_projects` to scan all active accounts and flags any project where volume or sentiment shifts unexpectedly.

Map customer issues to themes autonomously

The `list_feedback_themes` tool gives your classification agents a dictionary of existing customer issues. Instead of guessing, the agents map incoming feedback directly to your established Chattermill theme taxonomy. To refine this mapping, the agent queries `list_theme_categories` to understand the broader context of each theme. This ensures that new feedback submitted via `submit_feedback_response` is categorized accurately from the start.

Setup guide

Set up Chattermill MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Chattermill tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Chattermill Analyst",
    goal="Access and analyze Chattermill data via MCP.",
    backstory="Expert analyst with direct Chattermill access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Chattermill transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Chattermill Analyst",
        goal="Access and analyze Chattermill data via MCP.",
        backstory="Expert analyst with direct Chattermill access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Chattermill transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

Install `crewai[tools]` and pass your Vinkius HTTP URL directly into the agent's `mcps` array. This instantly exposes all 11 feedback tools to that specific agent.

Yes, you can use `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. This lets you restrict a writer agent to `submit_feedback_response` while keeping read tools private.

CrewAI's shared memory system allows agents to pass the output of `get_response_details` or `list_feedback_responses` to other crew members, enabling collaborative analysis without redundant API calls.

Yes, CrewAI supports Streamable HTTP, SSE, and stdio transports. You simply supply the secure Vinkius URL in your agent configuration to establish the connection.

Vinkius processes your customer feedback comments, sentiment metrics, and theme categories entirely within ephemeral sandboxes. The execution environment is completely zero-trust, ensuring no feedback data is stored or exposed to external parties.

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