How to Use the Userback MCP in Pydantic AI

Q: Can I list multiple Userback projects using Pydantic AI?

Yes. You call listuserbackprojects and the framework validates that the entire array of project objects meets your specified schema, guaranteeing reliable processing downstream.

Q: Does the Pydantic AI framework support complex Userback tool usage?

Absolutely. Whether you use listfeedbacks or getfeedbackdetails, the framework handles the complexity by validating the output, keeping your agent safe from unexpected API changes.

Ensures data accuracy when handling user feedback using the Pydantic AI framework and MCP Server.

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Connect Userback MCP to Pydantic AI

Create your Vinkius account to connect Userback to Pydantic AI 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 Pydantic AI

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Validate all Userback feedback records.

The agent uses `list_feedbacks` to pull a list of reports, but it validates that list against your schema first. If the API returns unexpected data fields, the Pydantic AI framework fails loudly—no silent corruption. If you need deeper info, running `get_feedback_details` guarantees that every returned field (like bug report IDs) matches your defined model.

Validate project and user listings via MCP Server.

You can use `list_userback_projects` to get all projects. Because everything is validated at runtime, you're guaranteed that the resulting list adheres exactly to your expected structure. Similarly, `list_account_users` returns data typed against Pydantic models, so you never have to worry about unexpected field names or types.

Get and validate project metadata.

Run `get_project_details` when your agent needs context on a specific Userback project. The key here is that the entire response—metadata, dates, IDs—is validated against Python types before the code runs. This prevents models from hallucinating fields or returning data in an unusable format. Correctness over speed; that's the point.

Setup guide

Set up Userback MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "userback-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Userback tools.",
)

result = await agent.run("List recent Userback transactions")
print(result.output)

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Common questions about Userback MCP in Pydantic AI

The framework wraps the MCP Server calls, forcing every response to conform to your Python models at runtime. This means your agent gets data it can trust and use immediately.

Yes. You call `list_userback_projects` and the framework validates that the entire array of project objects meets your specified schema, guaranteeing reliable processing downstream.

The server touches user account data, detailed feedback reports, and project identifiers. The Pydantic framework's job is to ensure the schema integrity of these specific data types.

Absolutely. Whether you use `list_feedbacks` or `get_feedback_details`, the framework handles the complexity by validating the output, keeping your agent safe from unexpected API changes.

The agent doesn't proceed. It throws a loud validation error because the data structure failed to match your Pydantic model, preventing bad data from ever getting into your application.

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