How to integrate Jira MCP with Mastra AI

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Introduction

This guide walks you through connecting Jira to Mastra AI using the Composio tool router. By the end, you'll have a working Jira agent that can create a new bug in project alpha, assign issue jira-102 to sarah lee, add comment to ticket jira-207 with update, start a new sprint for the dev board through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Jira account through Composio's Jira 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:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Jira tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Jira 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 Jira 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 Jira MCP server, and what's possible with it?

The Jira MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Jira account. It provides structured and secure access to your Jira projects, so your agent can perform actions like creating issues, managing sprints, commenting on tasks, assigning work, and tracking releases on your behalf.

  • Automated issue creation and tracking: Let your agent create new bugs, tasks, or stories, and keep tabs on issues across your Jira projects.
  • Collaborative commenting and updates: Have your agent add rich-text comments or attachments to issues, keeping team communication seamless and up to date.
  • Effortless assignment and watcher management: Easily assign issues to teammates or add watchers, ensuring everyone stays in the loop and accountable.
  • Sprint and release planning: Empower your agent to create sprints, manage boards, and organize project milestones or versions for agile teams.
  • Issue linking and bulk operations: Direct your agent to link related issues or perform bulk creation of tasks, streamlining project workflows and dependencies.

Supported Tools & Triggers

Tools
Triggers
Add AttachmentUploads and attaches a file to a jira issue.
Add CommentAdds a comment using atlassian document format (adf) for rich text to an existing jira issue.
Add Watcher to IssueAdds a user to an issue's watcher list by account id.
Assign IssueAssigns a jira issue to a user, default assignee, or unassigns; supports email/name lookup.
Bulk Create IssuesCreates multiple jira issues (up to 50 per call) with full feature support including markdown, assignee resolution, and priority handling.
Create IssueCreates a new jira issue (e.
Link IssuesLinks two jira issues using a specified link type with optional comment.
Create ProjectCreates a new jira project with required lead, template, and type configuration.
Create SprintCreates a new sprint on a jira board with optional start/end dates and goal.
Create VersionCreates a new version for releases or milestones in a jira project.
Delete CommentDeletes a specific comment from a jira issue using its id and the issue's id/key; requires user permission to delete comments on the issue.
Delete IssueDeletes a jira issue by its id or key.
Delete VersionDeletes a jira version and optionally reassigns its issues.
Delete WorklogDeletes a worklog from a jira issue with estimate adjustment options.
Edit IssueUpdates an existing jira issue with field values and operations.
Find UsersSearches for jira users by email, display name, or username to find account ids; essential for assigning issues, adding watchers, and other user-related operations.
Get All Issue Type SchemesRetrieves all jira issue type schemes with optional filtering and pagination.
Get all projectsRetrieves all visible projects using the modern paginated jira api with server-side filtering and pagination support.
Get Issue StatusesRetrieves all available issue statuses from jira with details.
Get All UsersRetrieves all users from the jira instance including active, inactive, and other user states with pagination support.
Get CommentRetrieves a specific comment by id from a jira issue with optional expansions.
Get Current UserRetrieves detailed information about the currently authenticated jira user.
Get IssueRetrieves a jira issue by id or key with customizable fields and expansions.
Get Issue Link TypesRetrieves all configured issue link types from jira.
Get Issue PropertyRetrieves a custom property from a jira issue by key.
Get Issue ResolutionsRetrieves all available issue resolution types from jira.
Get issue typesRetrieves all jira issue types available to the user using the modern api v3 endpoint; results vary based on 'administer jira' global or 'browse projects' project permissions.
Get Issue Type SchemeGets a jira issue type scheme by id with all associated issue types.
Get Issue WatchersRetrieves users watching a jira issue for update notifications.
Get Issue WorklogsRetrieves worklogs for a jira issue with user permission checks.
Get Project VersionsRetrieves all versions for a jira project with optional expansion.
Get Issue Remote LinksRetrieves links from a jira issue to external resources.
Get TransitionsRetrieves available workflow transitions for a jira issue.
Get Issue VotesFetches voting details for a jira issue; requires voting to be enabled in jira's general settings.
Get WorklogsRetrieves worklogs for a specified jira issue.
List BoardsRetrieves paginated jira boards with filtering and sorting options.
List Issue CommentsRetrieves paginated comments from a jira issue with optional ordering.
List SprintsRetrieves paginated sprints from a jira board with optional state filtering.
Move Issues to SprintMoves one or more jira issues to a specified active sprint.
Remove Watcher from IssueRemoves a user from an issue's watcher list by account id.
Search Issues Using JQL (GET)Searches for jira issues using jql with pagination and field selection.
Search Issues Using JQL (POST)Searches for jira issues using jql via post request for complex queries; ideal for lengthy jql queries that might exceed url character limits
Search issuesAdvanced jira issue search supporting structured filters and raw jql.
Send Notification for IssueSends a customized email notification for a jira issue.
Transition IssueTransitions a jira issue to a different workflow state, with support for transition name lookup and user assignment by email.
Update CommentUpdates text content or visibility of an existing jira comment.

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:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript

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 Jira through MCP.

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

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

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

Create a Tool Router session for Jira

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

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

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 Jira toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "jira-mastra-agent",
    instructions: "You are an AI agent with Jira 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

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: {
        jira: 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 Jira 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 Jira 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: ["jira"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "jira-mastra-agent",
    instructions: "You are an AI agent with Jira 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: { jira: 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 Jira 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

How to build Jira MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Jira MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Jira MCP with Claude Agent SDK

Google ADK

Google ADK

Use Jira MCP with Google ADK

LangChain

LangChain

Use Jira MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Jira MCP with Vercel AI SDK

LlamaIndex

LlamaIndex

Use Jira MCP with LlamaIndex

CrewAI

CrewAI

Use Jira MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with Mastra AI?

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 Jira tools.

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

Yes, absolutely. You can configure which Jira 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 Jira data and credentials are handled as safely as possible.

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