How to Use the Innform MCP in Pydantic AI

Q: How do I configure Pydantic AI to connect with Innform?

Install the slim package with MCP support, then initialize MCPToolset with your Vinkius HTTP URL. Pass this toolset directly to your agent constructor to start querying your training data via the MCP Server.

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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 Innform course data at runtime

The `list_courses` tool pulls your entire catalog of classes directly into your Python application. When using Pydantic AI, every course record returned by this tool is validated against strict types before your agent even sees it. This prevents corrupt data or missing fields from breaking your production workflows. If the API schema changes, the toolset fails loudly, allowing you to catch issues during integration tests rather than in production.

Track learner progress with absolute type safety

The `list_results` tool retrieves employee assessment scores and completion metrics for your agent to analyze. Pydantic AI ensures that grades, dates, and learner IDs match your exact schema expectations. Your agent can use this verified data to trigger automated emails or update internal HR databases. By combining it with `list_pathways`, you can build reliable tracking systems that never hallucinate compliance status.

Query organizational metadata securely

The `list_tags` tool fetches all administrative tags used to group your training materials and employees. Having these tags typed and validated makes it easy for your agent to organize content without errors. You can also call `list_departments` and `list_locations` to map out your company structure. This structured metadata helps your agent route training modules to the correct teams without manual intervention.

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-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, then initialize `MCPToolset` with your Vinkius HTTP URL. Pass this toolset directly to your agent constructor to start querying your training data via the MCP Server.

The framework guarantees that every payload returned by tools like `get_learner` conforms to your data models. If there is a schema mismatch, your code raises a validation error immediately instead of passing bad data to your agent.

Yes, the framework is model-agnostic. You can use this MCP Server with OpenAI, Anthropic, Gemini, or local models running on your own hardware.

Vinkius manages the authentication handshake for you. You only need to provide your single endpoint token in your environment variables when setting up the toolset connection.

Your department lists, location data, and learner results are processed in a zero-trust, ephemeral MCP environment. No personal data is cached, and your API keys are securely encrypted at rest.

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