How to Use the Innform MCP in Pydantic AI

Q: How do I configure the Innform MCP Server with Pydantic AI?

Install the slim package with MCP support and instantiate MCPToolset pointing to your Vinkius URL. Pass this toolset directly to your Agent constructor to expose tools like listusers and inviteuser.

Q: Can I use Pydantic AI to safely freeze overdue Innform learners?

Yes, you can write an agent that evaluates data from listoverdueassignments. The agent then calls freezeuser with validated parameters, ensuring only the correct, targeted accounts are locked.

Q: Does Pydantic AI support both HTTP and SSE transports for Innform?

Yes, the unified MCPToolset handles both streamable HTTP and SSE transports. This allows your agent to connect to the Vinkius hosting environment stably and run queries like listcompletedassignments reliably.

Run type-safe, validated LMS automations on Innform using the Pydantic AI framework.

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Connect Innform MCP to Pydantic AI

Create your Vinkius account to connect Innform 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 Innform with Pydantic AI

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Validate user operations with this Innform MCP Server

This MCP Server exposes `invite_user` and `update_user` to your Pydantic AI agents with strict runtime validation. If the server returns an unexpected user format, the framework raises a validation error immediately. This prevents corrupted data from entering your LMS when updating records. Your agent can safely run `get_user_details` knowing that every field matches your predefined Python schemas before your code executes.

Track assignments safely using Pydantic AI

Your agent calls `list_overdue_assignments` and `list_completed_assignments` to monitor training progress. Pydantic AI enforces type checks on the returned lists, preventing silent failures if the API schema changes. When parsing assignment deadlines or completion dates, the framework verifies the date formats automatically. You get clean, validated data that you can pipe into compliance dashboards with total confidence.

Guaranteed Account State Controls

Your agent uses `freeze_user` and `unfreeze_user` to control learner accounts based on activity. Pydantic AI ensures the user ID passed to these tools is structured correctly before making the API request. It also validates the response payload from `list_users` and `list_groups` to ensure no malformed group data slips through. This type-safe architecture protects your LMS from accidental bulk account lockouts.

Setup guide

Set up Innform 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": {
        "innform-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Innform 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 Innform. 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 Innform MCP in Pydantic AI

Install the slim package with MCP support and instantiate `MCPToolset` pointing to your Vinkius URL. Pass this toolset directly to your Agent constructor to expose tools like `list_users` and `invite_user`.

The framework fails loudly by raising a validation error during runtime. If `get_user_details` returns a missing field, the agent stops execution instead of letting corrupted data break your downstream application.

Yes, you can write an agent that evaluates data from `list_overdue_assignments`. The agent then calls `freeze_user` with validated parameters, ensuring only the correct, targeted accounts are locked.

Yes, the unified `MCPToolset` handles both streamable HTTP and SSE transports. This allows your agent to connect to the Vinkius hosting environment stably and run queries like `list_completed_assignments` reliably.

The MCP toolset validation checks all incoming user metadata and assignment statuses in memory. No learner emails or completion records are cached locally, and transport encryption secures every transaction between the agent and Vinkius.

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