How to Use the LearnUpon MCP in Pydantic AI

Q: How do I install the LearnUpon toolset for Pydantic AI?

Run pip install "pydantic-ai-slimmcp" and initialize the MCPToolset with your Vinkius server URL. Pass the toolset directly to your Agent constructor and install the MCP package via pip.

Q: How does the agent unenroll users safely?

The agent uses unenrolluser after confirming the user's active status via listusers. Pydantic AI validates the deletion confirmation schema to ensure the operation completed successfully.

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Connect LearnUpon MCP to Pydantic AI

Create your Vinkius account to connect LearnUpon 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 LearnUpon with Pydantic AI

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Type-safe user creation and updates

The `create_user` tool forces strict validation on every LearnUpon profile before it hits the API. If your Pydantic AI agent attempts to pass an invalid email format, the framework catches the error instantly. This prevents silent LearnUpon database corruption and keeps your user directory clean. Your Pydantic AI agent can safely run `update_user` knowing that any unexpected API response will trigger a loud validation failure.

Validate enrollments using the Pydantic AI framework

The `enroll_user_in_course` tool registers LearnUpon learners into specific classes with strict runtime schema checks. Every returned enrollment confirmation is validated against your pre-defined Python models. If a LearnUpon course ID is missing or malformed during a bulk sync, the Pydantic AI system halts. Use `list_courses` to pull the active catalog and verify that the target course exists before executing.

Securely query users and courses with MCP Server

The `search_users` tool allows your Pydantic AI agent to locate specific LearnUpon learner records using type-safe queries. By structuring the search parameters, the framework ensures the agent never passes null values. This LearnUpon integration operates over Streamable HTTP and SSE transports, connecting your Pydantic AI codebase to the Vinkius sandbox. Your agent can run on any LLM provider while maintaining strict type safety across all LMS operations.

Setup guide

Set up LearnUpon 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": {
        "learnupon-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about LearnUpon MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"` and initialize the `MCPToolset` with your Vinkius server URL. Pass the toolset directly to your `Agent` constructor and install the MCP package via pip.

The framework raises a validation error immediately, stopping the agent from acting on corrupted data. This is crucial when calling tools like `list_enrollments` where data integrity affects compliance records.

Yes, the framework is completely model-agnostic, letting you use Anthropic, Gemini, or local models. The type safety is handled at the Python layer, so the underlying LLM choice does not affect the MCP Server connection.

The agent uses `unenroll_user` after confirming the user's active status via `list_users`. Pydantic AI validates the deletion confirmation schema to ensure the operation completed successfully.

Your learner accounts and course completion data are processed in temporary, short-lived memory buffers. The Vinkius infrastructure uses end-to-end encryption and automatically destroys the execution environment as soon as the API call completes.

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