How to Use the Condeco (Eptura Engage) MCP in AutoGen
Run multi-agent debates in AutoGen to coordinate Condeco (Eptura Engage) room bookings and resolve scheduling conflicts.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Condeco (Eptura Engage) MCP to AutoGen
Create your Vinkius account to connect Condeco (Eptura Engage) 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.
Multi-agent room booking negotiation in AutoGen
Your AutoGen agents coordinate physical space allocations by calling `book_room` through the Condeco (Eptura Engage) MCP Server. A scheduling agent proposes a room, while a policy agent checks if the reservation complies with company rules before executing. This collaborative setup ensures that meeting spaces are booked efficiently. The agents use `get_room_availability` to resolve conflicts autonomously before writing any reservations to Condeco.
Automated desk cancellation and reallocation
The `cancel_desk_booking` tool lets your AutoGen agents manage hot desk allocations when team schedules shift. A coordination agent monitors user calendars and tells the booking agent to release a desk when an employee calls in sick. You register these capabilities using `mcp_server_tools` and pass them to your `AssistantAgent`. The agents talk to each other to ensure that `list_desks` is run and empty workspaces are reassigned to waiting employees.
Location verification and check-in tracking
The `check_in_to_location` tool allows your AutoGen security agent to verify physical attendance against office boundaries. When a user arrives, the agent runs `list_locations` to confirm the office capacity limits are respected. If a user fails to check in, the coordination agent can automatically run `cancel_room_booking` to free up the meeting room. This multi-agent loop keeps your physical office running smoothly without human oversight.
Set up Condeco (Eptura Engage) 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes Condeco (Eptura Engage) tools and returns structured results.
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="Condeco (Eptura Engage)_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Condeco (Eptura Engage) data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="Condeco (Eptura Engage)_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Condeco (Eptura Engage) data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Condeco. 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 Condeco (Eptura Engage) MCP in AutoGen
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