How to Use the BugHerd MCP in AutoGen

Debate your BugHerd feedback using AutoGen agents to reach consensus on task priority and resolution.

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Connect BugHerd MCP to AutoGen

Create your Vinkius account to connect BugHerd 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 BugHerd with AutoGen

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Debate BugHerd tasks with AutoGen agents

Let your agents argue over the status of a bug. One agent uses `list_tasks` to find high-priority items while another analyzes the impact. They reach a consensus before taking action. This ensures that only validated tasks move forward in your project workflow.

Update BugHerd status via AutoGen conversation

The agents decide when to run `update_task` based on their internal discussion. They challenge each other's conclusions to improve accuracy. You get a result that has been vetted by multiple perspectives. It removes the guesswork from managing your feedback queue.

Manage BugHerd projects with AutoGen

Use `list_projects` to give your agents full visibility into your organization. They coordinate efforts across different project boards. This multi-agent setup handles complex scenarios. They negotiate the best path for resolving feedback based on the current project constraints.

Setup guide

Set up BugHerd 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 BugHerd 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="BugHerd_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about BugHerd MCP in AutoGen

You pass the server tools directly into the AssistantAgent constructor. The adapter automatically handles the schema translation so the agents understand the available operations.

Absolutely. Your agents can call these tools to gather facts before they argue. They use the retrieved task data to support their positions.

Your data is secured by a zero-trust architecture. We only transmit the specific task and user information needed for the agent to complete its current turn.

The server supports both stdio and HTTP transports. This allows you to configure your agent environment exactly how you need it.

They can. If the agents converge on a decision to start new work, they will invoke the project creation tool to initialize your board.

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