How to Use the LearnWorlds MCP in Pydantic AI

Q: How do I set up the LearnWorlds MCP toolset in Pydantic AI?

Install pydantic-ai-slimmcp, instantiate MCPToolset with your Vinkius HTTP endpoint, and pass it to your Agent constructor. This automatically registers tools like getcoursecontents with full schema validation.

Q: What happens if LearnWorlds returns unexpected student data to Pydantic AI?

If a tool like getstudentdetails returns data that violates your Pydantic models, the framework raises a validation error immediately. This prevents your agent from processing corrupted data.

Q: Can I use Pydantic AI to automate LearnWorlds course enrollments?

Yes. Your agent can call enrollstudent to assign users to courses, with the framework validating the student ID and course ID parameters at runtime to prevent database errors.

Validate every LearnWorlds API payload at runtime using type-safe Pydantic AI agent toolsets.

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Connect LearnWorlds MCP to Pydantic AI

Create your Vinkius account to connect LearnWorlds 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 LearnWorlds with Pydantic AI

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Type-safe student creation with Pydantic AI

The `create_student` tool writes new user records to your LMS database. When you use Pydantic AI, the agent validates the student's email, username, and metadata against strict Python type models before sending the request. If the LLM generates a malformed email, the framework raises a validation error immediately. This prevents bad data from corrupting your LMS directory. The agent can then catch the error, fix the formatting, and retry the call safely without crashing your production system.

Bulletproof payment and billing reporting

The `list_payments` tool returns financial transaction histories from your online school. By linking this tool to Pydantic AI, you guarantee that every transaction amount, date, and user ID matches your expected database schema. The framework catches missing fields before they reach your reporting logic. If a payment record lacks a transaction ID, Pydantic AI fails loudly. This strict validation ensures your agent-generated financial reports are completely accurate and free of model hallucinations.

Strict subscription validation using this MCP Server

The `list_subscriptions` tool fetches active membership statuses for your student base. Connecting this MCP Server to your Pydantic AI agent ensures that subscription renewal dates and tier levels are parsed into native Python datetime objects and strict enums. Your code will never process a corrupted date format. You can safely run automation flows that call `enroll_student` based on these verified subscriptions. The type-safe schema validation acts as a shield, keeping your enrollment pipelines running smoothly without silent failures.

Setup guide

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

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

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

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Common questions about LearnWorlds MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`, instantiate `MCPToolset` with your Vinkius HTTP endpoint, and pass it to your `Agent` constructor. This automatically registers tools like `get_course_contents` with full schema validation.

If a tool like `get_student_details` returns data that violates your Pydantic models, the framework raises a validation error immediately. This prevents your agent from processing corrupted data.

Yes. Your agent can call `enroll_student` to assign users to courses, with the framework validating the student ID and course ID parameters at runtime to prevent database errors.

Yes, the integration supports both Streamable HTTP and SSE transports, allowing you to connect your Pydantic AI agents to externally running MCP servers.

It routes transaction details and course payloads through encrypted, isolated V8 sandboxes. Your database credentials are never exposed, and Pydantic AI validates all incoming payloads locally to prevent data leakage.

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