Innform MCP Server for Pydantic AIGive Pydantic AI instant access to 9 tools to Freeze User, Get User Details, Invite User, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Innform through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Innform app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 9 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Innform "
"(9 tools)."
),
)
result = await agent.run(
"What tools are available in Innform?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Innform MCP Server
Connect your Innform training portal to any AI agent and take full control of your Learning Management System (LMS) and employee compliance workflows through natural conversation.
Pydantic AI validates every Innform tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- User Lifecycle Orchestration — List all learners and admins, retrieve detailed high-fidelity profile metadata, and invite new users programmatically
- Assignment Intelligence — Programmatically monitor completed and overdue training assignments to maintain a perfectly coordinated compliance overview
- Training Group Architecture — Access your complete directory of learner groups and their properties to oversee your organizational training structure
- Access Control Management — Programmatically freeze or unfreeze learner accounts to manage platform access dynamically based on organizational needs
- Operational Monitoring — Verify API connectivity and monitor training progress directly through your agent for instant performance reporting
The Innform MCP Server exposes 9 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 9 Innform tools available for Pydantic AI
When Pydantic AI connects to Innform through Vinkius, your AI agent gets direct access to every tool listed below — spanning lms, employee-training, compliance-tracking, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Freeze a user account
Get details for a specific user
Invite a new learner
List completed training assignments
List learner groups
List overdue training assignments
List Innform users
Unfreeze a user account
Update an existing user
Connect Innform to Pydantic AI via MCP
Follow these steps to wire Innform into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Innform MCP Server
Pydantic AI provides unique advantages when paired with Innform through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Innform integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Innform connection logic from agent behavior for testable, maintainable code
Innform + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Innform MCP Server delivers measurable value.
Type-safe data pipelines: query Innform with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Innform tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Innform and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Innform responses and write comprehensive agent tests
Example Prompts for Innform in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Innform immediately.
"List all active learners in my Innform portal."
"Show me all overdue training assignments for the Engineering team."
"Freeze account for learner ID 'user_123' immediately."
Troubleshooting Innform MCP Server with Pydantic AI
Common issues when connecting Innform to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiInnform + Pydantic AI FAQ
Common questions about integrating Innform MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.