How to Use the Eventmaker MCP in AutoGen

Q: How do I load Eventmaker tools into AutoGen?

Install the autogen-extmcp package. You initialize the connection using mcpservertools with a StreamableHttpServerParams pointing to your URL. The framework automatically adapts the schemas.

Q: Can multiple AutoGen agents share the same Eventmaker tools?

Yes. You pass the tool list to the constructor of each AssistantAgent. The agents can independently call geteventdetaileddata to verify claims made by other agents during the conversation.

Deploy AutoGen agents to debate and manage Eventmaker operations. Resolve scheduling conflicts through consensus.

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Setup Eventmaker with AutoGen

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Resolve conflicts with AutoGen agents

The Eventmaker MCP Server gives your agents the data they need to debate floor layouts. One agent wants to maximize session capacity. Another wants to prevent fire code violations. They pull the active roster using `list_currently_ongoing_events`. The logistics agent runs `list_event_sessions` to check room assignments. The security agent counters by analyzing scan velocity. They negotiate a solution and output a final recommendation for your operations team.

Audit check-ins through consensus

The Eventmaker MCP standard feeds raw data to your agents for interpretation. Your primary agent fetches the scan records via `list_event_checkins_log`. It hands the dataset to an auditor agent designed to find anomalies in attendee behavior. The auditor runs `quick_event_engagement_audit` to cross-check the numbers. If the check-in volume doesn't match the active session count, the agents discuss the discrepancy until they identify the bottleneck.

Manage exhibitor ROI dynamically

This MCP integration pulls the partner list using `list_event_exhibitors` so your analytics agent can prove value to sponsors. A separate financial agent reviews the engagement metrics. They collaborate by feeding the exhibitor data into `get_event_performance_stats`. The agents debate which sponsors received the most foot traffic, generating a consensus report that strips out vanity metrics.

Setup guide

Set up Eventmaker 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 Eventmaker 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="Eventmaker_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Eventmaker 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="Eventmaker_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Eventmaker 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 Eventmaker. 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 Eventmaker MCP in AutoGen

Install the `autogen-ext[mcp]` package. You initialize the connection using `mcp_server_tools` with a `StreamableHttpServerParams` pointing to your URL. The framework automatically adapts the schemas.

Yes. You pass the tool list to the constructor of each `AssistantAgent`. The agents can independently call `get_event_detailed_data` to verify claims made by other agents during the conversation.

You define a planner agent that governs API access. Instead of letting all agents spam the endpoint, the planner dictates when the group is allowed to execute `list_event_participants`.

No. The server handles all the underlying authentication and routing. Your agents just see standard Python functions. They pass the required parameters, and the server returns the JSON payload.

When agents analyze physical location data and timestamps from the check-in logs, they query an ephemeral container hosted on Vinkius. The zero-trust architecture ensures the request is authenticated with a single endpoint token. The container vanishes the moment the API returns the payload, preventing data leakage.

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