How to Use the eduMe MCP in Pydantic AI

Q: How do I register the eduMe MCP Server in Pydantic AI?

Install the package with pip install "pydantic-ai-slimmcp" and initialize MCPToolset with your Vinkius URL. Pass this toolset to your Agent's toolsets parameter.

Build type-safe training workflows in Pydantic AI with strict runtime validation of learning records.

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Connect eduMe MCP to Pydantic AI

Create your Vinkius account to connect eduMe 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 eduMe with Pydantic AI

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Discover mobile training courses with Pydantic AI

To fetch your catalog, the `list_training_courses` tool retrieves the complete list of mobile training programs available in your account. Your Pydantic AI agent invokes this tool to match active modules against your application's internal data models. Because this framework enforces strict type validation, any unexpected API schema changes will trigger a runtime error immediately. This keeps your automated training workflows from processing corrupted or missing course data.

Enforce strict schemas on trainee profiles

Pulling data with the `get_user_training_profile` tool outputs the full training history and metadata for a specific learner. Your Pydantic AI agent parses this payload directly into validated Python types before executing any business logic. If the tool returns an unexpected null value or an altered date format, the validation layer halts the run. This strictness ensures your compliance reporting engine never writes malformed records to your production database.

Validate team audits via MCP Server

To run a fast check, the `quick_team_training_audit` tool generates a high-level summary of active teams and their completion rates. Your Pydantic AI agent uses the server via the MCP toolset helper to run background compliance checks on your workforce. You register the server using the `MCPToolset` helper, which automatically maps the tool schemas to Pydantic models. Your agent can confidently query team statuses, knowing every integer and string matches your code's expectations.

Setup guide

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

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

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

Get your connection token →

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

Install the package with `pip install "pydantic-ai-slim[mcp]"` and initialize `MCPToolset` with your Vinkius URL. Pass this toolset to your Agent's `toolsets` parameter.

The framework raises a validation error at runtime. This prevents your agent from making decisions based on malformed trainee profiles or incomplete course lists.

Yes. The framework is model-agnostic, meaning you can run your validated training audits using OpenAI, Anthropic, or local models via Ollama.

The integration supports both Streamable HTTP and SSE transports. Vinkius manages the server hosting, so your application only needs to connect to the provided HTTP endpoint.

Nowhere on our end. Vinkius operates on a zero-trust model where all trainee profile details, course completion records, and team assignments are processed in an ephemeral V8 Isolate sandbox that leaves zero footprint.

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