How to Use the Moodle MCP in Pydantic AI

Q: How do I set up the Moodle MCP Server within Pydantic AI?

Install pydantic-ai-slimmcp and use the MCPToolset class pointing to your Vinkius HTTP endpoint. Pass the toolset directly to your agent's constructor.

Q: Does Pydantic AI validate the output of Moodle tools like getcoursegrades?

Yes, every response from getcoursegrades is validated against runtime schemas. If the LMS returns unexpected fields, the framework catches it instantly.

Q: How does this toolset handle connection errors to the LMS?

The MCPToolset handles HTTP transport failures gracefully. It raises standard Python exceptions that your application can catch and retry.

Build type-safe LMS integrations using Pydantic AI to validate student grades and course completion data at runtime.

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Connect Moodle MCP to Pydantic AI

Create your Vinkius account to connect Moodle 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 Moodle with Pydantic AI

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Type-safe grade validation with Pydantic AI

Your agent calls `get_course_grades` to pull raw academic scores into a strictly typed environment. Pydantic AI validates the structure of every gradebook entry before the model can process it. If the LMS API returns an unexpected data format, the MCP validation layer raises an error immediately. This prevents your agent from making decisions based on corrupted or malformed grade values.

Secure messaging with this MCP Server

This MCP Server provides `send_message` to let your agent contact students directly when they miss deadlines. The input payload is validated against strict schemas to ensure no invalid user IDs are targeted. Your agent will never send a message to the wrong recipient. The Pydantic AI validation layer checks the recipient fields against user records fetched via `get_users`.

Strict course tracking pipelines

The agent uses `get_course_completion` and `get_course_assignments` to verify which students have completed their required tasks. It maps these completions to structured Python models for downstream analysis. This ensures that your automated reporting pipelines are completely predictable. You can run nightly audits across multiple courses without worrying about silent model hallucinations.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Moodle. 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 Moodle MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and use the `MCPToolset` class pointing to your Vinkius HTTP endpoint. Pass the toolset directly to your agent's constructor.

Yes, every response from `get_course_grades` is validated against runtime schemas. If the LMS returns unexpected fields, the framework catches it instantly.

Yes, the toolset is model-agnostic. You can run your LMS automation using OpenAI, Anthropic, Gemini, or even local models.

The `MCPToolset` handles HTTP transport failures gracefully. It raises standard Python exceptions that your application can catch and retry.

Data is processed in memory and validated against transient schemas. Vinkius secures the connection end-to-end, ensuring that student rosters and quiz scores are never stored or logged on external servers.

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