How to Use the Innform MCP in OpenAI Agents SDK

Q: Can I use this MCP Server to assign training pathways?

This server focuses on retrieving data using tools like listpathways and listcourses. It allows your agent to read current assignments and monitor progress, but does not support writing new records yet.

Build production-ready agents that manage your Innform training pipelines using the OpenAI Agents SDK and our MCP Server.

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Connect Innform MCP to OpenAI Agents SDK

Create your Vinkius account to connect Innform to OpenAI Agents SDK 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 OpenAI Agents SDK

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Claude

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Sync training data with OpenAI Agents SDK

The `list_results` tool lets your agent pull completed assessments and scores straight into your workspace. By running this query, your pipeline can instantly spot which team members completed their safety paths and which ones need a nudge. Our integration handles the heavy lifting by exposing raw JSON payloads directly to your agent. It parses through `list_modules` to map out the exact lessons your team is taking, keeping your database updated without manual CSV exports.

Auto-discover Innform tools in your Python agent

The `get_learner` tool fetches detailed profiles of individual employees directly inside your OpenAI runtime. When you pass the MCP Server configuration to your agent constructor, the SDK automatically registers this tool along with all other endpoints. No manual schema mapping is required on your end. Your agent can instantly call `list_learners` to search your active workforce or trace user progress across your entire organization.

Map organizational structures instantly

The `list_departments` tool queries your company's internal divisions to organize training metrics by team. This lets your agent run targeted compliance checks on specific cohorts instead of scanning the entire company database. By combining this with `list_locations` and `list_tags`, your agent can group learners by physical office or job role. It makes routing the right course pathways to the right people a simple, automated task.

Setup guide

Set up Innform MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Innform tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Innform tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Innform tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Innform Agent",
            instructions="You have access to Innform tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Innform MCP in OpenAI Agents SDK

You install the library, then instantiate the HTTP server class with your Vinkius endpoint. Pass that server instance into your agent constructor to let it auto-discover the tools.

Yes. You can restrict tool access directly in your agent configuration or use OpenAI's built-in guardrails to validate tool calls before they execute.

The SDK relies on standard HTTP handling, but you should configure `cacheToolsList=True` to prevent redundant schema lookups. This keeps your API consumption low and avoids hitting rate limits during busy shifts.

This server focuses on retrieving data using tools like `list_pathways` and `list_courses`. It allows your agent to read current assignments and monitor progress, but does not support writing new records yet.

Yes, because Vinkius runs your MCP Server in an isolated V8 sandbox. Your learner records and course results are never stored or used to train public models.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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