How to Use the Linear MCP in AutoGen

Q: How do AutoGen agents execute Linear tools?

You register the tools using the mcpservertools adapter and pass them to your AssistantAgent. When the agents decide on an action, they execute tools like createlinearissue directly through the MCP Server.

Q: Can I limit which AutoGen agents can modify Linear issues?

Yes, you can assign tools like updatelinearissue only to specific execution agents in your AutoGen group chat. This prevents planning agents from making unauthorized changes to your backlog.

Let AutoGen agents debate sprint priorities and update tickets using this Linear MCP Server.

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Setup Linear with AutoGen

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Multi-agent debate on Linear MCP Server issues

Set up an AutoGen conversation where a product agent and an engineering agent negotiate ticket priority. They query active tasks using `list_linear_issues` to evaluate current workloads. Once they agree on the priority, the execution agent runs `update_linear_issue` to modify the ticket. This ensures no issue gets escalated without a balanced, automated review.

Automated cycle planning via agent agreement

Let your agents handle the tedious parts of sprint planning. One agent checks team availability with `list_linear_users` while another pulls active cycles via `list_linear_cycles`. They discuss the best distribution of tasks and use `create_linear_issue` to assign them. You get a fully coordinated sprint plan generated through multi-agent collaboration.

Consensus-driven ticket triaging

When a new bug is reported, your AutoGen agents can analyze it together. They use `list_linear_labels` and `list_linear_teams` to determine which team is best suited to handle the work. After the agents reach consensus on the correct team and labels, they call `create_linear_comment` to document their reasoning on the ticket. This keeps your triage process transparent and documented.

Setup guide

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

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

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

You register the tools using the mcp_server_tools adapter and pass them to your AssistantAgent. When the agents decide on an action, they execute tools like `create_linear_issue` directly through the MCP Server.

Yes, you can assign tools like `update_linear_issue` only to specific execution agents in your AutoGen group chat. This prevents planning agents from making unauthorized changes to your backlog.

The agents use their conversational loop to resolve conflicts before calling `update_linear_issue`. If a conflict occurs, they can run `get_linear_issue` to fetch the latest state and renegotiate.

Yes, the server fully supports streamable HTTP connections through Vinkius. This allows your AutoGen agents to invoke tools like `check_linear_status` with minimal latency during active conversations.

All data transit is encrypted, and execution happens in isolated V8 sandboxes. Your actual comments, issue descriptions, and team lists are never stored or analyzed by this MCP Server.

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