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Userback MCP Server for Pydantic AIGive Pydantic AI instant access to 6 tools to Create Feedback Entry, Get Feedback Details, Get Project Details, and more

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Userback through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Ask AI about this App Connector for Pydantic AI

The Userback app connector for Pydantic AI is a standout in the Developer Tools category — giving your AI agent 6 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Userback "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Userback?"
    )
    print(result.data)

asyncio.run(main())
Userback
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* 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 Userback MCP Server

Connect your Userback account to any AI agent and simplify how you collect and manage visual feedback, bug reports, and user suggestions through natural conversation.

Pydantic AI validates every Userback tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

  • Feedback Management — List all feedback entries and retrieve detailed metadata, screenshots, and comments for specific reports.
  • Project Control — List and query feedback projects to keep your development and design work organized.
  • Direct Creation — Programmatically create new feedback entries or bug reports for specific projects via AI.
  • Team Visibility — List account users and collaborators to understand your organization's review team.
  • Status Tracking — Monitor the progress of feedback items and verify if issues have been resolved.

The Userback MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 6 Userback tools available for Pydantic AI

When Pydantic AI connects to Userback through Vinkius, your AI agent gets direct access to every tool listed below — spanning visual-feedback, bug-reporting, user-experience, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

create_feedback_entry

Create a new feedback entry

get_feedback_details

Get details for a specific feedback

get_project_details

Get details for a specific project

list_account_users

List account users

list_feedbacks

List Userback feedbacks

list_userback_projects

List Userback projects

Connect Userback to Pydantic AI via MCP

Follow these steps to wire Userback into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install Pydantic AI

Run pip install pydantic-ai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 6 tools from Userback with type-safe schemas

Why Use Pydantic AI with the Userback MCP Server

Pydantic AI provides unique advantages when paired with Userback through the Model Context Protocol.

01

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

02

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Userback integration code

03

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

04

Dependency injection system cleanly separates your Userback connection logic from agent behavior for testable, maintainable code

Userback + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Userback MCP Server delivers measurable value.

01

Type-safe data pipelines: query Userback with guaranteed response schemas, feeding validated data into downstream processing

02

API orchestration: chain multiple Userback tool calls with Pydantic validation at each step to ensure data integrity end-to-end

03

Production monitoring: build validated alert agents that query Userback and output structured, schema-compliant notifications

04

Testing and QA: use Pydantic AI's dependency injection to mock Userback responses and write comprehensive agent tests

Example Prompts for Userback in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Userback immediately.

01

"List all feedback projects in my Userback account."

02

"Show me the latest bug reports for the 'Product App v2' project."

03

"Create a new suggestion: 'Add dark mode support' to project '10293'."

Troubleshooting Userback MCP Server with Pydantic AI

Common issues when connecting Userback to Pydantic AI through the Vinkius, and how to resolve them.

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Userback + Pydantic AI FAQ

Common questions about integrating Userback MCP Server with Pydantic AI.

01

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
02

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
03

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Userback MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.