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How to Use the edX MCP in Pydantic AI

Build type-safe student management workflows that validate course data at runtime with Pydantic AI.

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Works with every AI agent you already use

…and any MCP-compatible client

edX MCP on Cursor AI Code Editor MCP Client edX MCP on Claude Desktop App MCP Integration edX MCP on OpenAI Agents SDK MCP Compatible edX MCP on Visual Studio Code MCP Extension Client edX MCP on GitHub Copilot AI Agent MCP Integration edX MCP on Google Gemini AI MCP Integration edX MCP on Lovable AI Development MCP Client edX MCP on Mistral AI Agents MCP Compatible edX MCP on Amazon AWS Bedrock MCP Support
MCP Servers — Included with Plan
Vinkius runs on Pydantic AI

Connect edX MCP to Pydantic AI

Create your Vinkius account to connect edX to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Key Capabilities

Strict validation for student enrollment states

Your agent uses `enroll_in_course` to register students, with every response validated against rigorous Pydantic models. If the underlying API returns unexpected fields, Pydantic AI raises a validation error immediately. This prevents corrupt enrollment states from silently breaking your database. The agent either gets clean, verified data or halts execution to protect system integrity.

Type-safe grade tracking with zero hallucinations

Fetch academic performance using `get_user_grades` and `get_course_grades` without worrying about model hallucinations. The toolset enforces strict type constraints on the scores, course IDs, and student records returned by the server. If your model tries to invent a grade format, the runtime validation catches it. This guarantees that your automated grading reports rely on actual, structured data.

Validate profile updates through this MCP Server

Update student profiles safely by routing `update_user_profile` through a validated MCP toolset. The Pydantic AI agent ensures all input parameters match the expected schema before hitting the server. You read current settings using `get_user_preferences` to verify format alignment. This architecture keeps your student database clean and prevents malformed profile fields.

Setup guide

Set up edX MCP in Pydantic AI

Prerequisites

  • Python 3.10+ installed
  • pydantic-ai-slim[fastmcp] package
  • Active Vinkius subscription with a valid endpoint token
  1. 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. 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. 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": {
        "edx-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by edX. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about edX MCP in Pydantic AI

Pass our hosted MCP endpoint URL to the `MCPToolset` constructor and register it in your agent's `toolsets` list. Do not use the deprecated `MCPServerHTTP` class, as the unified toolset is the standard approach.
Pydantic AI validates the response from `get_course_blocks` at runtime. If a block is missing a required field, the framework throws a validation error, preventing your agent from processing corrupt curriculum structures.
Yes, Pydantic AI is model-agnostic. You can pair this server with local models or commercial APIs, and the framework will still enforce strict schema validation on tools like `list_courses`.
Call `list_enrollments` through your validated agent. The framework parses the returned JSON list into strongly-typed Python objects, making it easy to filter active learning paths safely.
We run the server inside a zero-trust V8 isolate sandbox that isolates student enrollment records. No record data is logged or cached on our servers, ensuring your compliance with academic privacy standards remains intact.

Start using the edX MCP today

We host it, we monitor it, we maintain it. You just paste one token.

Built & Managed by Vinkius 30s setup 10 tools

We've already built the connector for edX. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
All 10 tools are live and waiting. You're up and running in seconds.

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