How to Use the Linear MCP in AutoGen

Q: Can I use AutoGen to track Linear priority across teams?

Yes, agents can call listteams and then searchissues across those teams. They can then identify bottlenecks and flag them for your human review.

Q: Can AutoGen agents create new Linear issues?

They can. Using the createissue tool, an agent can generate a ticket after concluding that a new task is required by the project roadmap.

Task multiple AutoGen agents with debating and managing your Linear issues for consensus-driven engineering.

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Setup Linear with AutoGen

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Debate Linear issues in AutoGen

Create specialized agents that discuss the best way to handle a ticket. One agent can review the technical requirements via `get_issue`, while another evaluates priority using `list_cycles`. They negotiate the next steps before taking action. This ensures every `update_issue` call is verified by your team's internal standards.

Collaborative project updates

Assign an agent to monitor your backlog using `list_issues`. When it detects a new ticket, it can propose a plan that other agents review. Once they reach a consensus, the system can use `create_comment` to update stakeholders. It removes the guesswork from issue management.

Agentic team management

Use your agents to query organizational structure with `list_teams`. They can decide which team is best suited to handle a new request based on previous project history. This allows for autonomous assignment of tasks across your Linear workspace. Your agents handle the overhead of routing while you focus on code.

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

Agents share the toolset provided by the MCP Server. By using `list_issues` and `get_issue`, they gather the necessary context to debate and finalize changes through `update_issue`.

Yes, agents can call `list_teams` and then `search_issues` across those teams. They can then identify bottlenecks and flag them for your human review.

The connection is strictly scoped to your Linear API token. The Vinkius ephemeral environment ensures that your sensitive issue tracking data is never exposed or logged outside your session.

They can. Using the `create_issue` tool, an agent can generate a ticket after concluding that a new task is required by the project roadmap.

The MCP Server manages the underlying calls. You should configure your agents to avoid excessive polling, but the integration respects standard API constraints.

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