How to Use the Innform MCP in AutoGen

Let autonomous AutoGen agents debate compliance gaps and audit your Innform training records via MCP.

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Connect Innform MCP to AutoGen

Create your Vinkius account to connect Innform to AutoGen 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 AutoGen

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Run multi-agent debates on training gaps

One agent checks the rules, another checks the data. By using this MCP Server, your AutoGen agents can divide and conquer compliance auditing. A compliance agent calls `list_pathways` to see what is required, while an audit agent pulls `list_results` to see what actually happened. They compare notes in a group chat. If they find a discrepancy, they pull specific details using `get_learner` to figure out exactly who needs to complete their training.

Audit regional training demands using AutoGen

Tracking training requirements across multiple offices is a logistical nightmare. Your coordinator agent can call `list_locations` and `list_departments` to lay out the company map. A second agent runs `list_learners` to assign employees to their respective offices. Together, they negotiate which departments are at risk of non-compliance based on the active curriculum.

Verify LMS access controls autonomously with AutoGen MCP Server

Security audits shouldn't require manual API checks. An agent can run `get_me` to verify the active connection profile and ensure the toolset has the correct permissions. It can then cross-reference this profile with the active metadata in `list_tags` to confirm the agent only accesses authorized content. This keeps your automated workflows within strict boundaries.

Setup guide

Set up Innform MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Innform tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Innform_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Innform data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Innform_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Innform data")
    print(result.messages[-1].content)

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 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 AutoGen

You use the AutoGen MCP extension to pull the tools from your Vinkius server. The adapter handles the schema conversion so agents can immediately use tools like `list_courses`.

No, this server does not support write actions. Agents can only read data using tools like `list_modules` and `list_pathways` to analyze current training states.

The agents debate the results in their chat session. One agent might pull results via `list_results` while another looks up the learner using `get_learner` to resolve the conflict.

It uses the streamable HTTP transport. This allows your multi-agent system to securely query the LMS API endpoints from any environment.

All calls run through a secure, zero-trust sandbox on Vinkius. Your learner records and assessment scores are protected by ephemeral execution environments that destroy session data immediately after use.

Use it with your favorite AI tools

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