How to Use the DeskTime MCP in AutoGen

Q: How do AutoGen agents connect to DeskTime?

Install the autogen-extmcp package and configure your parameters with the Vinkius endpoint. Pass the resulting tools to your AssistantAgent constructor.

Q: Can an agent delete projects automatically?

Yes, if you give it permission. An agent can call removeproject if it determines an initiative is dead, though you might want to add a human-in-the-loop approval step.

Build AutoGen multi-agent systems that debate DeskTime productivity scores and manage project timelines.

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Connect DeskTime MCP to AutoGen

Create your Vinkius account to connect DeskTime 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 DeskTime with AutoGen

Ask AI about this MCP

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Perplexity

Multi-agent workforce analysis

Give your AutoGen agents the ability to argue about resource allocation. A project manager agent can call `get_project_details` to check the current status. Meanwhile, an HR agent runs `get_employee_performance` to see who is overworked. They negotiate a solution and agree on who gets assigned the next ticket.

AutoGen MCP Server setup

You need the standard extension package to get started. Use the provided tools function with a StreamableHttpServerParams configuration pointing to your Vinkius URL. The framework handles the schema conversion automatically through McpToolAdapter. You just hand the tool list to your AssistantAgent and let the conversation begin.

Autonomous task management

Your agents can handle the boring administrative work while you focus on actual engineering. One agent monitors a shared inbox and triggers `create_new_task` when a bug report comes in. A different agent periodically checks `list_project_tasks` to find stale items. It reaches out to the assigned developer, confirms the fix, and runs `mark_task_completed`.

Setup guide

Set up DeskTime 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 DeskTime 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="DeskTime_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the autogen-ext[mcp] package and configure your parameters with the Vinkius endpoint. Pass the resulting tools to your AssistantAgent constructor.

Yes. You can share the tool list across your entire agent group. One agent can run `list_projects` while another executes `get_company_info`.

It supports both stdio and Streamable HTTP transports. Just point the client at your Vinkius URL and the adapter handles the rest.

Yes, if you give it permission. An agent can call `remove_project` if it determines an initiative is dead, though you might want to add a human-in-the-loop approval step.

Only your authenticated AI client. Vinkius wraps the MCP connection in a strict V8 isolate sandbox. When agents pull company info or active employee lists, the memory space is completely wiped after the request finishes.

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