How to integrate Linear MCP with Mastra AI

This guide walks you through connecting Linear to Mastra 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 Mastra 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 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

Introduction

This guide walks you through connecting Linear to Mastra 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 Mastra 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

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKLlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Linear tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Linear tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Linear agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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:

  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

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Linear through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for Linear

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["linear"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Linear MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "linear" for Linear access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Linear toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "linear-mastra-agent",
    instructions: "You are an AI agent with Linear tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        linear: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Linear toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

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

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["linear"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      linear: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "linear-mastra-agent",
    instructions: "You are an AI agent with Linear tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { linear: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Linear through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows
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 logo

ChatGPT

Use Linear MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Linear MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Linear MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Linear MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Linear MCP with Claude Cowork

Codex logo

Codex

Use Linear MCP with Codex

Cursor logo

Cursor

Use Linear MCP with Cursor

OpenClaw logo

OpenClaw

Use Linear MCP with OpenClaw

Hermes logo

Hermes

Use Linear MCP with Hermes

CLI logo

CLI

Use Linear MCP with CLI

Google ADK logo

Google ADK

Use Linear MCP with Google ADK

LangChain logo

LangChain

Use Linear MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Linear MCP with Vercel AI SDK

LlamaIndex logo

LlamaIndex

Use Linear MCP with LlamaIndex

CrewAI logo

CrewAI

Use Linear MCP with CrewAI

OpenCode logo

OpenCode

Use Linear MCP with OpenCode

VS Code logo

VS Code

Use Linear MCP with VS Code

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. Mastra 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.

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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.

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Abhi Arya
Co-founder, Opennote

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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.

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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.

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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.

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Co-Founder, Ingram Technologies