How to Use the Linear MCP in OpenAI Agents SDK

Q: How do I connect this Linear MCP Server to my OpenAI Agents SDK pipeline?

Install openai-agents via pip and configure the streamable HTTP server transport pointing to the Vinkius MCP endpoint. Pass the server instance directly into your Agent constructor using the mcpservers list. The SDK automatically discovers all 12 tools, including listissues and createissue, without manual schema writing.

Q: Can my OpenAI Agents SDK handle issue comments on Linear?

Yes, the agent uses the createcomment tool to append updates directly to any ticket. It supports full Markdown, meaning your agent can format code blocks, add strikethrough text, and ping team members with @mentions.

Q: How does the OpenAI Agents SDK prevent rate limits when querying Linear?

Set cacheToolsList=True during the server initialization block in your Python script. This prevents the SDK from repeatedly querying the schema. For heavy read operations, have your agent use searchissues with specific text filters instead of pulling down the entire backlog.

Q: What happens if my agent tries to assign a ticket to an invalid user?

The execution fails. The tool updateissue requires valid UUIDs for fields like assignees. To avoid this, configure your agent to call listteams or getissue first to verify user identities and team keys before attempting modification.

Ship code faster by letting your OpenAI Agents SDK build, track, and update Linear issues directly from production workflows.

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Connect Linear MCP to OpenAI Agents SDK

Create your Vinkius account to connect Linear 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 with OpenAI Agents SDK

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Automated issue triaging for OpenAI Agents SDK

This Linear MCP Server lets your OpenAI Agents SDK instantiate and organize tasks directly on your team's kanban board. The agent uses `list_teams` to identify the correct engineering team, checks existing tasks with `search_issues`, and creates fresh tickets using `create_issue` complete with priority levels and custom descriptions. Because the OpenAI Agents SDK features built-in guardrails, you can set hard limits on which agents can modify critical tickets. The SDK validates the agent's parameters before invoking `update_issue`, giving you a safety net that prevents rogue models from messing with your roadmap.

Context-aware cycle and project tracking

The server exposes `list_cycles` and `list_projects` to supply your OpenAI Agent with real-time sprint data. When a customer reports a bug, your agent checks the active cycle metrics to see if the fix can slot into the active sprint or needs to go to the backlog. You don't have to write custom glue code to fetch this metadata. The SDK auto-discovers these tools on startup, letting your agent inspect project structures with `get_project` and match them against customer feedback instantly.

Zero-config tools for OpenAI Agents SDK

This MCP Server connects to your Python agent setup with a single HTTP streamable endpoint. Your agent runs `get_viewer` on startup to verify its API token, ensuring it has the correct permissions before executing any write operations across your tracker. Once connected, the agent appends context to active tickets by calling `create_comment` with markdown formatting. This keeps your developers in the loop without forcing your agent to drop out of its execution loop to write a status update.

Setup guide

Set up Linear 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 tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Linear 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 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 Agent",
            instructions="You have access to Linear tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 OpenAI Agents SDK

Install `openai-agents` via pip and configure the streamable HTTP server transport pointing to the Vinkius MCP endpoint. Pass the server instance directly into your Agent constructor using the `mcp_servers` list. The SDK automatically discovers all 12 tools, including `list_issues` and `create_issue`, without manual schema writing.

Yes, the agent uses the `create_comment` tool to append updates directly to any ticket. It supports full Markdown, meaning your agent can format code blocks, add strikethrough text, and ping team members with @mentions.

Set `cacheToolsList=True` during the server initialization block in your Python script. This prevents the SDK from repeatedly querying the schema. For heavy read operations, have your agent use `search_issues` with specific text filters instead of pulling down the entire backlog.

The execution fails. The tool `update_issue` requires valid UUIDs for fields like assignees. To avoid this, configure your agent to call `list_teams` or `get_issue` first to verify user identities and team keys before attempting modification.

The server processes your Linear issue data, comments, and user details inside an isolated V8 sandbox on Vinkius. Your API token is never exposed to the LLM. It stays encrypted in transit, and the agent only sees the raw text payloads returned by tools like `get_issue`.

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