How to Use the Venue MCP in AutoGen

Q: How does AutoGen use Venue tools for planning?

You can set up a group where one agent plans using listevents, and another critiques the plan by calling geteventdetails. They debate until they converge on the most accurate, viable event setup.

Q: Can AutoGen use Venue tools to list recordings?

Yes. You can configure an agent pair: one calls listrecordings and the other confirms if those recordings are relevant by cross-referencing them with data from geteventdetails. They argue over inclusion criteria.

Q: What if I need AutoGen to find all available venues?

The agents can collaborate: one calls listevents, and another systematically loops through the results, calling geteventdetails sequentially until all necessary information is gathered for a report.

Q: If I need AutoGen to check account info related to Venue?

You give the agents access to getme. They can debate what permissions they actually need by calling this tool multiple times and comparing the outputs against their assigned roles.

Make venues work through debate with AutoGen multi-agent systems.

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Setup Venue with AutoGen

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Perplexity

Debate event requirements and logistics.

You can set up a system where one agent checks the initial plan using `list_events`, while another challenges it by calling `get_event_analytics`. They debate whether the current venue has enough engagement history for the proposed scale. This consensus-driven approach is perfect for complex planning. The agents negotiate details—like comparing attendee numbers from `list_event_registrations` against event goals.

Coordinate multi-step speaker and event setup.

Build a pipeline where the 'Planner Agent' calls `create_event`, which then triggers the 'Logistics Agent'. The latter, needing to fill out details, uses `list_speakers` to check for existing talent before finally calling `create_speaker`. The agents talk to each other until they agree on all necessary tool parameters and successful execution.

Validate venue data across multiple sources.

A 'Validator Agent' can be tasked with checking if an event is viable. It first gets the basic details using `get_event_details`, then calls `list_sessions` to check content availability, and finally uses that combined information to suggest necessary updates. This debate ensures all facets—from session listing to core event data—are vetted before final approval.

Setup guide

Set up Venue 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 Venue 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="Venue_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Venue MCP in AutoGen

You can set up a group where one agent plans using `list_events`, and another critiques the plan by calling `get_event_details`. They debate until they converge on the most accurate, viable event setup.

Yes. You can configure an agent pair: one calls `list_recordings` and the other confirms if those recordings are relevant by cross-referencing them with data from `get_event_details`. They argue over inclusion criteria.

The MCP Server doesn't expose contract tools, but you can make agents negotiate the required steps. For example, one agent might call `list_event_registrations` to determine necessary contact details for a manual contract process.

The agents can collaborate: one calls `list_events`, and another systematically loops through the results, calling `get_event_details` sequentially until all necessary information is gathered for a report.

You give the agents access to `get_me`. They can debate what permissions they actually need by calling this tool multiple times and comparing the outputs against their assigned roles.

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