How to Use the Frontegg MCP in AutoGen

Q: How do I initialize Frontegg MCP tools in AutoGen?

Install the autogen-extmcp package. Pass your endpoint URL through StreamableHttpServerParams to get the tools, then hand them to your AssistantAgent constructor.

Q: Can multiple AutoGen agents share the Frontegg toolset?

You should assign specific tools to specific agents. Give listtenants to an analysis agent, and restrict deleteuser strictly to an execution agent that requires consensus to act.

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Create your Vinkius account to connect Frontegg 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 Frontegg with AutoGen

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The `create_tenant` and `create_user` tools plug directly into AutoGen's multi-agent conversation framework. You can assign these execution capabilities to a specific provisioning agent while keeping other agents strictly read-only. This setup allows your agents to debate the necessity of an action before running it. A security agent might review a request, check the current state, and require approval before the provisioning agent actually fires the creation command.

Negotiate account deletions.

Removing an account is risky, so you can build a workflow where agents deliberate over the `delete_tenant` tool. A compliance agent runs `get_tenant_details` to check for active contracts while a separate execution agent waits for consensus. If the compliance agent flags an active billing plan, it challenges the deletion request. The system only invokes the final removal tool when all participating agents agree the action is safe.

Audit roles through consensus.

You can set up a group chat where agents use `list_system_roles` and `list_permissions` to evaluate your security posture. One agent pulls the raw role data while another cross-references it against best practices. This MCP integration lets the user watch the deliberation happen in real-time. They discuss any discrepancies found in the active assignments pulled via `list_users` and formulate a final recommendation on access changes.

Setup guide

Set up Frontegg 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 Frontegg 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="Frontegg_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="Frontegg_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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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 Frontegg. 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 Frontegg MCP in AutoGen

Install the `autogen-ext[mcp]` package. Pass your endpoint URL through `StreamableHttpServerParams` to get the tools, then hand them to your AssistantAgent constructor.

You should assign specific tools to specific agents. Give `list_tenants` to an analysis agent, and restrict `delete_user` strictly to an execution agent that requires consensus to act.

The built-in adapter handles schema conversion automatically. Your agents understand exactly what parameters to pass into `create_tenant` without manual typing on your end.

Yes. You can configure a security agent to challenge any proposed changes to `list_permissions` before a separate administrator agent actually executes the modification.

Vinkius manages the authentication layer completely outside your client. When agents discuss credentials pulled via `list_m2m_tokens`, the network request routes through an ephemeral container that terminates instantly. No keys sit on our servers.

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