How to Use the Linear (Issue Tracking & PM) MCP in AutoGen

Q: How does AutoGen resolve conflicts when managing Linear (Issue Tracking & PM) issues?

The agents debate the priority of issues retrieved via getissue. They use their configured system prompts to negotiate and reach a consensus before taking action.

Let AutoGen agents debate priorities, assign tickets, and manage your Linear cycles through collaborative conversation.

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Connect Linear (Issue Tracking & PM) MCP to AutoGen

Create your Vinkius account to connect Linear (Issue Tracking & PM) 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 Linear (Issue Tracking & PM) with AutoGen

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Let AutoGen agents debate ticket priority

The `list_issues` tool feeds recent workspace tickets into your AutoGen agent conversation. One agent can review the backlog while another argues for bug fixes, using actual ticket data to reach a consensus. This collaborative approach ensures that no critical issue is ignored. Your agents use `get_issue` to dive deep into specific ticket details before deciding which tasks to prioritize next.

Manage team boundaries with this MCP Server

The `list_teams` tool allows your agent cluster to understand how your workspace is structured. A coordinator agent queries this tool to determine which team should handle an incoming feature request. By checking team boundaries alongside `list_users`, the agents negotiate assignments based on active workspace members. This prevents tickets from being assigned to inactive accounts or the wrong departments.

Coordinate sprint planning in AutoGen

The `list_cycles` tool provides the active sprint boundaries needed for multi-agent planning sessions using this MCP Server. Your product manager agent and tech lead agent debate how many issues to pull into the current cycle. They use `list_labels` to categorize the work first, ensuring that technical debt and feature work are balanced. The agents then finalize the plan and update your workspace.

Setup guide

Set up Linear (Issue Tracking & PM) 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 Linear (Issue Tracking & PM) 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="Linear (Issue Tracking & PM)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Linear (Issue Tracking & PM) 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="Linear (Issue Tracking & PM)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Linear (Issue Tracking & PM) data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Linear. 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 Linear (Issue Tracking & PM) MCP in AutoGen

You register the server tools with your AssistantAgent. The agents then call tools like `list_issues` during their group chats to gather real-time workspace data.

Yes, any agent in your group chat can call the registered tools. For example, one agent might run `list_projects` while another checks `list_cycles` to coordinate.

The agents debate the priority of issues retrieved via `get_issue`. They use their configured system prompts to negotiate and reach a consensus before taking action.

It supports both stdio and Streamable HTTP transports. You can use the mcp_server_tools adapter to handle schema conversion automatically.

We run every server instance in a zero-trust, isolated environment. Your issue tracking limits, user lists, and team records are accessed via secure tokens and never exposed to third parties.

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