How to Use the Userback MCP in CrewAI

Q: How can I set up a Userback feedback loop using CrewAI?

You assign an agent to use createfeedbackentry. The crew's shared memory tracks the submission, and another specialized agent can follow up by checking related project details.

Q: What if I need to check user accounts with CrewAI and Userback?

You can configure an agent to run listaccountusers. The results are added to the shared memory, making them available for subsequent analysis by other agents.

Q: When using this MCP Server, what data type is touched by Userback?

The core data handled here involves feedback entries, which contain detailed visual bug reports. Your autonomous operations process these records.

Q: Can I get details on a specific report with CrewAI and Userback?

You use the getfeedbackdetails tool, passing in a unique ID. The result is returned to your crew, allowing them to analyze the specific data point immediately.

Build autonomous feedback operations and reporting with CrewAI and the Userback MCP Server.

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Create your Vinkius account to connect Userback 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 Userback with CrewAI

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Automated Feedback Triage

The `create_feedback_entry` tool allows a specialized agent to automatically log new user bug reports. A monitor agent can watch for these submissions, while an action agent takes the necessary follow-up steps. If a report needs more data, your crew can use `get_project_details` before escalating it.

Mapping Project Structures

Need to know what projects are in scope? Use the `list_userback_projects` tool. This provides a definitive list of Userback projects that your crew can reference for context. If you need specific details on one project, simply pass the ID into this MCP Server for full data retrieval.

Auditing Account Users and Reports

A dedicated agent can run `list_account_users` to compile a roster of all users. Another specialized agent uses `list_feedbacks` to gather every submitted report. This lets your autonomous crew build an audit trail, knowing who reported what and when.

Setup guide

Set up Userback 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 Userback tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Userback 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="Userback Analyst",
        goal="Access and analyze Userback data via MCP.",
        backstory="Expert analyst with direct Userback access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Userback 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 Userback. 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 Userback MCP in CrewAI

You assign an agent to use `create_feedback_entry`. The crew's shared memory tracks the submission, and another specialized agent can follow up by checking related project details.

Yes. Passing the MCP Server URL allows your team of agents to use `list_userback_projects` as a foundational step in their operations.

You can configure an agent to run `list_account_users`. The results are added to the shared memory, making them available for subsequent analysis by other agents.

The core data handled here involves `feedback entries`, which contain detailed visual bug reports. Your autonomous operations process these records.

You use the `get_feedback_details` tool, passing in a unique ID. The result is returned to your crew, allowing them to analyze the specific data point immediately.

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