How to Use the Leiga MCP in AutoGen

Q: How do I configure AutoGen to use the Leiga MCP Server?

Install the extension with pip install -U "autogen-extmcp". Use mcpservertools with your Vinkius HTTP URL to load the tools, then pass them to your AssistantAgent.

Q: How do AutoGen agents handle Leiga workflow automation?

Agents can query your automated processes using listworkflows. They can then use this information to debate whether a task is moving through the correct pipeline stages.

Deploy AutoGen agents that use the Leiga MCP Server to debate and manage tasks, sprints, and team workloads.

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Setup Leiga with AutoGen

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Multi-Agent Sprint Consensus with Leiga

The Leiga MCP Server enables AutoGen agents to collaborate on complex project planning. You can set up a product manager agent that calls `list_tasks` to find pending work, while a developer agent uses `list_team_members` to flag capacity constraints before agreeing on assignments. The agents debate the priority of each item in the backlog. Once they reach a consensus on who should take the work, a coordinator agent calls `create_task` to finalize the ticket, reducing manual coordination overhead.

Automated Workflow Auditing with AutoGen

Use this Leiga MCP Server setup to build an automated auditing team. One agent checks project configurations with `get_project_details`, while another runs `list_workflows` to verify that team processes are being followed. If a workflow is broken or a high-priority task is stuck in the wrong state, the agents discuss the issue. They can automatically pull the ticket details with `get_task_details` and recommend a fix to the team lead.

Team Workload Resolution

Resolve team bottlenecks through agent negotiation using Leiga tools. An allocation agent uses `list_organizational_teams` to monitor team distribution, while a resource agent checks individual workloads. When an imbalance is detected, the agents propose reassigning tasks to balance the load. They verify the changes against active project structures, ensuring assignments stay realistic and aligned with team goals.

Setup guide

Set up Leiga 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 Leiga 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="Leiga_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the extension with `pip install -U "autogen-ext[mcp]"`. Use `mcp_server_tools` with your Vinkius HTTP URL to load the tools, then pass them to your `AssistantAgent`.

Yes, one agent can draft the task requirements, another can run `list_team_members` to find an assignee, and a third can execute `create_task` to build the ticket.

Agents can query your automated processes using `list_workflows`. They can then use this information to debate whether a task is moving through the correct pipeline stages.

Yes, the adapter supports both stdio and streamable HTTP transports, allowing your agents to communicate with the server regardless of your hosting setup.

Your project structures, task details, and workflow configurations are processed in a secure, ephemeral V8 sandbox. No data is stored, ensuring your proprietary agile structures remain completely private.

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