How to integrate Linear MCP with Pydantic AI

This guide walks you through connecting Linear to Pydantic AI 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 through natural language commands. This guide will help you understand how to give your Pydantic AI 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.

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Linear

Oauth2Api Key

Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.

32 Tools3 Triggers

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Introduction

This guide will help you understand how to give your Pydantic AI 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.

Also integrate Linear with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

How to set up your Composio API key and User ID
How to create a Composio Tool Router session for Linear
How to attach an MCP Server to a Pydantic AI agent
How to stream responses and maintain chat history
How to build a simple REPL-style chat interface to test your Linear workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

Type Safety: Built on Pydantic for automatic data validation
MCP Support: Native support for Model Context Protocol servers
Streaming: Built-in support for streaming responses
Async First: Designed for async/await patterns

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.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

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

Step-by-step Guide

Step by step09 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account with an active API key
Basic familiarity with Python and async programming

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

Log in to the Composio dashboard.
Navigate to your API settings and generate a new API key.
Store this key securely as you'll need it for authentication.

Install dependencies

bash

pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

composio connects your agent to external SaaS tools like Linear
pydantic-ai lets you create structured AI agents with tool support
python-dotenv loads your environment variables securely from a .env file

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates your agent to Composio's API
USER_ID associates your session with your account for secure tool access
OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

What's happening:

We load environment variables and import required modules
Composio manages connections to Linear
MCPServerStreamableHTTP connects to the Linear MCP server endpoint
Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Linear
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["linear"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

What's happening:

We're creating a Tool Router session that gives your agent access to Linear tools
The create method takes the user ID and specifies which toolkits should be available
The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python

# Attach the MCP server to a Pydantic AI Agent
linear_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[linear_mcp],
    instructions=(
        "You are a Linear assistant. Use Linear tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)

What's happening:

The MCP client connects to the Linear endpoint
The agent uses GPT-5 to interpret user commands and perform Linear operations
The instructions field defines the agent's role and behavior

Build the chat interface

python

# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Linear.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()

What's happening:

The agent reads input from the terminal and streams its response
Linear API calls happen automatically under the hood
The model keeps conversation history to maintain context across turns

Run the application

python

if __name__ == "__main__":
    asyncio.run(main())

What's happening:

The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Linear and Pydantic AI:

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Linear
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["linear"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    linear_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[linear_mcp],
        instructions=(
            "You are a Linear assistant. Use Linear tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Linear.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Linear through Composio's Tool Router. With this setup, your agent can perform real Linear actions through natural language. You can extend this further by:

Adding other toolkits like Gmail, HubSpot, or Salesforce
Building a web-based chat interface around this agent
Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Linear for workflow automation)

This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

TOOLS & TRIGGERS

Supported Tools and Triggers

Every Linear action and event your agent gets out of the box.

Create attachment

Creates a new attachment and associates it with a specific, existing Linear issue.

Add reaction to comment

Tool to add a reaction to an existing Linear comment.

Create a comment

Creates a new comment on a specified Linear issue.

Create linear issue

Creates a new issue in a specified Linear project and team, requiring team_id and title, and allowing optional properties like description, assignee, state, priority, cycle, and due date.

Create issue relation

Create a relationship between two Linear issues using the issueRelationCreate mutation.

Create a label

Creates a new label in Linear for a specified team, used to categorize and organize issues.

Create Project

Creates a new Linear project with specified name and team associations.

Create Project Milestone

Tool to create a project milestone in Linear with a name and optional target date and sort order.

Create Project Update

Tool to create a project status update post for a Linear project.

Delete issue

Archives an existing Linear issue by its ID, which is Linear's standard way of deleting issues; the operation is idempotent.

Download issue attachments

Downloads a specific attachment from a Linear issue; the `file_name` must include the correct file extension.

Get current user

Gets the currently authenticated user's ID, name, email, and other profile information — this is the account behind the API token, which may be a bot or service account rather than a human user.

Get cycles by team ID

Retrieves all cycles for a specified Linear team ID; cycles are time-boxed work periods (like sprints).

Get create issue default params

Fetches a Linear team's default issue estimate and state, useful for pre-filling new issue forms.

Get Linear issue

Retrieves an existing Linear issue's comprehensive details, including id, identifier, title, description, timestamps, state, team, creator, attachments, comments (with user info and timestamps, use issue.

Get Linear project

Retrieves a single Linear project by its unique identifier.

List issue drafts

Tool to list issue drafts.

List issues by team ID

Tool to list all issues for a specific Linear team, scoped by team ID.

Get all cycles

Retrieves all cycles (time-boxed sprint iterations) org-wide from the Linear account; no filters applied.

List Linear issues

Lists non-archived Linear issues; if project_id is not specified, issues from all accessible projects are returned.

Get labels

Retrieves labels from Linear.

List linear projects

Retrieves all projects from the Linear account.

List Linear states

Retrieves all workflow states for a specified team in Linear, representing the stages an issue progresses through in that team's workflow.

Get teams

Retrieves all teams with their members and projects.

List Linear users

Lists all workspace users (not team-scoped) with their IDs, names, emails, and active status.

Remove label from Linear issue

Removes a specified label from an existing Linear issue using their IDs; successful even if the label isn't on the issue.

Remove reaction from comment

Tool to remove a reaction on a comment.

Run Query or Mutation

Execute any GraphQL query or mutation against Linear's API.

Search Linear issues

Search Linear issues using full-text search across identifier, title, and description.

Update issue

Updates 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.

Update a comment

Tool to update an existing Linear comment's body text.

Update Project

Tool to update an existing Linear project.

FRAMEWORKS

How to build Linear MCP Agent with another framework

ChatGPT

Use Linear MCP with ChatGPT

OpenAI Agents SDK

Use Linear MCP with OpenAI Agents SDK

Claude Agent SDK

Use Linear MCP with Claude Agent SDK

Claude Code

Use Linear MCP with Claude Code

Claude Cowork

Use Linear MCP with Claude Cowork

Codex

Use Linear MCP with Codex

Cursor

Use Linear MCP with Cursor

OpenClaw

Use Linear MCP with OpenClaw

Hermes

Use Linear MCP with Hermes

CLI

Use Linear MCP with CLI

Google ADK

Use Linear MCP with Google ADK

LangChain

Use Linear MCP with LangChain

Vercel AI SDK

Use Linear MCP with Vercel AI SDK

Mastra AI

Use Linear MCP with Mastra AI

LlamaIndex

Use Linear MCP with LlamaIndex

CrewAI

Use Linear MCP with CrewAI

OpenCode

Use Linear MCP with OpenCode

VS Code

Use Linear MCP with VS Code

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Google Sheets

Oauth2

Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.

Notion

Oauth2Api Key

Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.

Airtable

Oauth2Api Key

Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.

Asana

Oauth2

Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.

FAQ

Frequently asked questions

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.

Yes, you can. Pydantic AI 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.

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.

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.

Start with Linear.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Linear tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Linear MCP with Pydantic AI

Connect Linear without auth hassles

Introduction

Also integrate Linear with

TL;DR

What is Pydantic AI?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Create a Tool Router Session

Initialize the Pydantic AI Agent

Build the chat interface

Run the application

Complete Code

Conclusion

Supported Tools and Triggers

How to build Linear MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Google Sheets

Notion

Airtable

Asana

Frequently asked questions

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

Can I use Tool Router MCP with Pydantic AI?+

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

How safe is my data with Composio Tool Router?+

Start with Linear.It takes 30 seconds.

Teams ship real agents on Composio