How to Use the Linear (Issue Tracking & PM) MCP in OpenAI Agents SDK

Q: What is the best way to handle pagination when listing issues?

The listissues tool returns the most recent issues from your workspace. For massive backlogs, have your agent run iterative queries or filter by team using listteams first. This keeps payload sizes small and processing times fast.

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Connect Linear (Issue Tracking & PM) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Linear (Issue Tracking & PM) to OpenAI Agents SDK 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 OpenAI Agents SDK

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Track backlogs directly through OpenAI Agents SDK

Your agent uses `list_issues` to pull the latest unassigned bugs directly from your workspace backlog. It then calls `list_users` to check who has bandwidth and routes tasks based on real-time team workloads. This keeps the team aligned without constant status meetings. You can also deploy `get_issue` to pull deep context on specific blockers so your agent can update stakeholders automatically.

Run sprint updates automatically

Monitoring active sprint cycles is simple when your agent calls `list_cycles` to track deadlines. It extracts start and end boundaries to calculate remaining team velocity on the fly without manual spreadsheet tracking. Pair this with `list_projects` to keep high-level milestones updated. The agent checks if current cycle tasks align with active project goals and alerts you if a milestone is slipping.

Clean up labels and teams across your workspace

The agent relies on `list_teams` and `list_labels` to audit how your workspace is organized. It scans for duplicate labels or abandoned teams that clutter up your views. It uses `get_viewer` to verify its own API permissions first. This ensures the agent never attempts to read or modify data outside its designated boundary.

Setup guide

Set up Linear (Issue Tracking & PM) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Linear (Issue Tracking & PM) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Linear (Issue Tracking & PM) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Linear (Issue Tracking & PM) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Linear (Issue Tracking & PM) Agent",
            instructions="You have access to Linear (Issue Tracking & PM) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Linear (Issue Tracking & PM) MCP in OpenAI Agents SDK

Install the SDK first using pip. Then, initialize the streamable HTTP client pointing to your Vinkius MCP Server endpoint. Pass this server instance directly to your agent constructor to auto-discover all eight Linear tools.

This specific MCP Server focuses on reading and auditing your workspace. It exposes tools like `list_issues` and `get_issue` to gather context. If you need creation tools, you can pair it with a write-enabled server in your agent's toolset.

The SDK automatically queries the endpoint to list available tools. Once connected, your agent instantly knows how to use `list_cycles` and `list_projects` without manual schema definitions. Set the cache flag to true to speed up subsequent agent runs.

The `list_issues` tool returns the most recent issues from your workspace. For massive backlogs, have your agent run iterative queries or filter by team using `list_teams` first. This keeps payload sizes small and processing times fast.

Yes, your sensitive issue descriptions and viewer credentials never persist on Vinkius. The server runs in a secure, ephemeral V8 isolate sandbox. All API requests to Linear are direct and encrypted, ensuring your team's internal roadmaps remain private.

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