How to integrate Linear MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Linear to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Linear agent that can create a new bug for team mobile, add a comment to issue lin-123, list all cycles for the design team, download the latest attachment from issue lin-456 through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Linear account through Composio's Linear MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Linear
  • Configure an AI agent that can use Linear as a tool
  • Run a live chat session where you can ask the agent to perform Linear operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

What is the Linear MCP server, and what's possible with it?

The Linear MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Linear account. It provides structured and secure access to your team's issues, projects, and workflows, so your agent can perform actions like creating issues, posting comments, managing attachments, organizing teams, and automating project tracking on your behalf.

  • Automated issue creation and management: Instantly create new Linear issues, update existing ones, or archive issues to keep your team’s backlog organized and up to date.
  • Commenting and collaboration: Post comments on issues, facilitate team discussions, and keep everyone in the loop without manual effort.
  • Attachment handling: Add or download attachments to and from issues, making it easy to share files or reference important documents right from Linear.
  • Team and cycle insights: Retrieve all teams, fetch cycles (sprints) by team ID, and get default issue parameters to help your agent contextualize and optimize planning activities.
  • Personalized workspace access: Identify the current user, fetch their profile information, and tailor actions or queries to individual team members for smarter automation.

Supported Tools & Triggers

Tools
Triggers
Create linear attachmentCreates a new attachment and associates it with a specific, existing linear issue.
Create a commentCreates a new comment on a specified linear issue.
Create linear issueCreates a new issue in a specified linear project and team, requiring a title and description, and allowing for optional properties like assignee, state, priority, cycle, and due date.
Get create issue default paramsFetches a linear team's default issue estimate and state, useful for pre-filling new issue forms.
Create a labelCreates a new label in linear for a specified team, used to categorize and organize issues.
Delete issueArchives an existing linear issue by its id, which is linear's standard way of deleting issues; the operation is idempotent.
Get all teamsRetrieves all teams from the linear workspace without requiring any parameters.
Download issue attachmentsDownloads a specific attachment from a linear issue; the `file name` must include the correct file extension.
Get current userGets the currently authenticated user's id, name, email, and other profile information.
Get cycles by team IDRetrieves all cycles for a specified linear team id; cycles are time-boxed work periods (like sprints) and the team id must correspond to an existing team.
Get Linear issueRetrieves an existing linear issue's comprehensive details, including title, description, attachments, and comments.
Get all cyclesRetrieves all cycles (time-boxed iterations for work) from the linear account; no filters are applied.
List Linear issuesLists non-archived linear issues; if project id is not specified, issues from all accessible projects are returned.
Get labels by teamRetrieves all labels associated with a given team id in linear; the team id must refer to an existing team.
List linear projectsRetrieves all projects from the linear account.
List Linear statesRetrieves all workflow states for a specified team in linear, representing the stages an issue progresses through in that team's workflow.
Get teams by projectRetrieves all teams, including their members, and filters each team's associated projects by the provided 'project id'.
List Linear usersLists all users in the linear workspace with their ids, names, emails, and active status.
Remove label from Linear issueRemoves a specified label from an existing linear issue using their ids; successful even if the label isn't on the issue.
Run Query or MutationWildcard action that executes any graphql query or mutation against the linear api.
Update issueUpdates an existing linear issue using its `issue id`; requires at least one other attribute for modification, and all provided entity ids (for state, assignee, labels, etc.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Linear project
  • Some knowledge of Python or Typescript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Linear.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Linear Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["linear"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only linear.
  • The router checks the user's Linear connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Linear.
  • This approach keeps things lightweight and lets the agent request Linear tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Linear. "
        "Help users perform Linear operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Linear and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Linear operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Linear.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Linear and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["linear"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Linear. "
        "Help users perform Linear operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Linear MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Linear.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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Use Linear MCP with LlamaIndex

CrewAI

CrewAI

Use Linear MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Linear MCP?

With a standalone Linear MCP server, the agents and LLMs can only access a fixed set of Linear tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Linear and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Linear tools.

Can I manage the permissions and scopes for Linear while using Tool Router?

Yes, absolutely. You can configure which Linear scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Linear data and credentials are handled as safely as possible.

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