How to integrate Jira MCP with Vercel AI SDK v6

This guide walks you through connecting Jira to Vercel AI SDK v6 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 through natural language commands. This guide will help you understand how to give your Vercel AI SDK 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.

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Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently.

94 Tools3 Triggers

Introduction

This guide walks you through connecting Jira to Vercel AI SDK v6 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 through natural language commands.

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

Also integrate Jira with

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle ADKLangChainMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Jira integration
  • Using Composio's Tool Router to dynamically load and access Jira tools
  • Creating an MCP client connection using HTTP transport
  • Building an interactive CLI chat interface with conversation history management
  • Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

  • streamText: Core function for streaming responses with real-time tool support
  • MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
  • Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
  • OpenAI Provider: Native integration with OpenAI models

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.

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 you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
2

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.
3

Install required dependencies

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

First, install the necessary packages for your project.

What you're installing:

  • @ai-sdk/openai: Vercel AI SDK's OpenAI provider
  • @ai-sdk/mcp: MCP client for Vercel AI SDK
  • @composio/core: Composio SDK for tool integration
  • ai: Core Vercel AI SDK
  • dotenv: Environment variable management
4

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

  • OPENAI_API_KEY: Your OpenAI API key for GPT model access
  • COMPOSIO_API_KEY: Your Composio API key for tool access
  • COMPOSIO_USER_ID: A unique identifier for the user session
5

Import required modules and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

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

if (!process.env.OPENAI_API_KEY) 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,
});
What's happening:
  • We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
  • The dotenv/config import automatically loads environment variables
  • The MCP client import enables connection to Composio's tool server
6

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["jira"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Jira tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned mcp object contains the URL and authentication headers needed to connect to the MCP server
  • This session provides access to all Jira-related tools through the MCP protocol
7

Connect to MCP server and retrieve tools

typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
What's happening:
  • We're creating an MCP client that connects to our Composio Tool Router session via HTTP
  • The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
  • The type: "http" is important - Composio requires HTTP transport
  • tools() retrieves all available Jira tools that the agent can use
8

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to jira, like summarize my last 5 emails, send an email, etc... :)))\n",
);

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

rl.prompt();
What's happening:
  • We initialize an empty messages array to maintain conversation history
  • A readline interface is created to accept user input from the command line
  • Instructions are displayed to guide the user on how to interact with the agent
9

Handle user input and stream responses with real-time tool feedback

typescript
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({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

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

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • We use streamText instead of generateText to stream responses in real-time
  • toolChoice: "auto" allows the model to decide when to use Jira tools
  • stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
  • onStepFinish callback displays which tools are being used in real-time
  • We iterate through the text stream to create a typewriter effect as the agent responds
  • The complete response is added to conversation history to maintain context
  • Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Jira and Vercel AI SDK:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

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

if (!process.env.OPENAI_API_KEY) 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,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["jira"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to jira, like summarize my last 5 emails, send an email, etc... :)))\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({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

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

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a Jira agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

  • Real-time streaming responses for a better user experience with typewriter effect
  • Live tool execution feedback showing which tools are being used as the agent works
  • Dynamic tool loading through Composio's Tool Router with secure authentication
  • Multi-step tool execution with configurable step limits (up to 10 steps)
  • Comprehensive error handling for robust agent execution
  • Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS & TRIGGERS

Supported Tools and Triggers

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

Add Attachment

Uploads and attaches a file to a Jira issue.

Add Comment

Adds a comment using Atlassian Document Format (ADF) for rich text to an existing Jira issue.

Add Users to Project Role

Adds users and optionally groups to a project role.

Add User to Group

Adds a user to a Jira group.

Add Watcher to Issue

Adds a user to an issue's watcher list by account ID.

Add Worklog

Tool to add a worklog entry to a Jira issue.

Analyse Jira Expression

Analyses Jira expressions for syntax validation, type checking, and complexity analysis.

Assign Issue

Assigns a Jira issue to a user, default assignee, or unassigns; supports email/name lookup.

Bulk Create Issues

Creates multiple Jira issues (up to 50 per call) with full feature support including markdown, assignee resolution, and priority handling.

Check User Permissions

Check user permissions for global and project-level operations in Jira.

Create Group

Creates a new group in Jira with the specified name.

Create Issue

Creates a new Jira issue (e.

Link Issues

Links two Jira issues using a specified link type with optional comment.

Get JQL Autocomplete Data

Retrieves JQL autocomplete reference data including reserved words, field names, and function names.

Create Project

Creates a new Jira project with required lead, template, and type configuration.

Create Sprint

Creates a new sprint on a Jira board with optional start/end dates and goal.

Create Version

Creates a new version for releases or milestones in a Jira project.

Delete Comment

Deletes 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 Issue

Permanently and irreversibly deletes a Jira issue by its ID or key.

Delete Version

Deletes a Jira version and optionally reassigns its issues.

Delete Worklog

Deletes a worklog from a Jira issue with estimate adjustment options.

Edit Issue

Updates an existing Jira issue with field values and operations.

Evaluate Jira Expression

Tool to evaluate Jira expressions using the enhanced search API.

Bulk Fetch Issues

Tool to bulk fetch multiple Jira issues by their IDs or keys (max 100 per call).

Find Users 2

Tool to find users in Jira by query string, account ID, or property search.

Find Users For Picker

Find users for picker components by matching query against user attributes like display name and email.

Get All Groups

Retrieves all groups from the Jira instance with pagination support.

Get All Issue Type Schemes

Retrieves all Jira issue type schemes with optional filtering and pagination.

Get all projects

Retrieves all visible projects using the modern paginated Jira API with server-side filtering and pagination support.

Get Issue Statuses

Retrieves all issue statuses associated with workflows from Jira.

Get All Users

Retrieves all users from the Jira instance including active, inactive, app accounts, and system accounts, with pagination support.

Get Attachment

Retrieves the binary content of a Jira attachment by ID.

Get Attachment Meta

Tool to retrieve Jira attachment settings including upload limits and enabled status.

Get Comment

Retrieves a specific comment by ID from a Jira issue with optional expansions.

Get Component

Tool to retrieve components from Jira projects with search and filtering.

Get Create Field Metadata for Issue Type

Tool to retrieve field metadata for a specific issue type in a project.

Get Current User

Retrieves detailed information about the currently authenticated Jira user.

Get Dashboards

Tool to list and search Jira dashboards visible to the current user.

Get Favorite Filters

Tool to retrieve favorite filters for the current user.

Get fields

Tool to retrieve Jira issue fields metadata.

Get custom fields paginated

Tool to retrieve Jira fields in pages.

Get Filter

Retrieves a specific Jira saved filter by ID, including its JQL and sharing metadata, to reuse in subsequent searches.

Get Group

Retrieves details of a specific Jira group by name or ID.

Get Service Management Info

Retrieves runtime information for the Jira Service Management instance.

Get Issue

Retrieves a Jira issue by ID or key with customizable fields and expansions.

Get Create Issue Metadata

Tool to retrieve issue creation metadata for Jira projects.

Get Issue Edit Meta

Tool to retrieve editable fields for a Jira issue.

Get Issue Link Types

Retrieves all configured issue link types from Jira.

Get issue picker

Tool to get issue picker suggestions from Jira.

Get Issue Property

Retrieves a custom property from a Jira issue by key.

Get Issue Resolutions

Retrieves all available issue resolution types from Jira.

Get issue types

Retrieves 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 Watchers

Retrieves users watching a Jira issue for update notifications.

Get JQL autocomplete reference data

Tool to retrieve JQL autocomplete reference data.

Get JQL autocomplete suggestions

Tool to get JQL field auto-complete suggestions.

Get My Permissions

Tool to retrieve the user's permissions in Jira.

Get User Locale Preference

Tool to retrieve the locale preference of the currently authenticated Jira user.

Get Permissions

Tool to retrieve all available Jira permissions.

Get Permitted Projects

Tool to retrieve projects where the current user has specific permissions.

Get Project

Retrieves details of a Jira project by its ID or key.

Get Project Roles

Retrieves all available roles for a Jira project.

Get Project Type

Retrieves detailed information about a specific Jira project type by its key.

Get Project Versions

Retrieves all versions for a Jira project with optional expansion.

Get Recent Projects

Retrieves a list of projects recently accessed by the authenticated user.

Get Issue Remote Links

Retrieves links from a Jira issue to external resources.

Get Server Info

Tool to retrieve Jira instance server information.

Get Service Desk Request Type Fields

Tool to retrieve JSM request type field metadata for filling out portal requests.

Get System Avatars

Tool to retrieve all system avatars for a specific type (issuetype, project, user, or priority).

Get Transitions

Retrieves available workflow transitions for a Jira issue.

Get Universal Avatar Type Owner

Tool to retrieve all avatars (system and custom) for a specific type and entity in Jira.

Get Universal Avatar View Type

Tool to retrieve the default avatar image for a specific type (project, issuetype, or priority) from Jira.

Get Avatar Image

Tool to retrieve a specific avatar image by type and ID from Jira.

Get Issue Votes

Fetches voting details for a Jira issue; requires voting to be enabled in Jira's general settings.

Get Worklogs

Retrieves worklogs for a specified Jira issue.

List All Projects

Tool to list all projects accessible to the user.

List Boards

Retrieves paginated Jira boards with filtering and sorting options.

List Comments by IDs

Tool to retrieve multiple comments by their IDs in a single request.

List Jira Filters

Tool to search and list Jira saved filters (saved searches) visible to the current user.

List Groups (Picker)

Tool to search and list groups using Jira's picker endpoint.

List Issue Comments

Retrieves paginated comments from a Jira issue with optional ordering.

List Project Types

Retrieves all Jira project types available in the instance.

List Sprints

Retrieves paginated sprints from a Jira board with optional state filtering.

Move Issues to Sprint

Moves one or more Jira issues to a specified active sprint.

Parse JQL Queries

Parse and validate JQL queries, returning their abstract syntax tree structure along with any errors or warnings.

Remove User from Group

Removes a user from a Jira group.

Remove User from Project Role

Removes a user or group from a project role.

Remove Watcher from Issue

Removes a user from an issue's watcher list by account ID.

Search Approximate Count

Count issues matching a JQL query using approximate count endpoint.

Search Dashboards

Tool to search for Jira dashboards with filtering, sorting, and pagination support.

Search Issues Using JQL (GET)

Searches for Jira issues using JQL with pagination and field selection.

Search issues

Advanced Jira issue search supporting structured filters and raw JQL.

Send Notification for Issue

Sends a customized email notification for a Jira issue.

Transition Issue

Transitions a Jira issue to a different workflow state, with support for transition name lookup and user assignment by email.

Update Comment

Updates text content or visibility of an existing Jira comment.

FRAMEWORKS

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Cursor

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OpenClaw logo

OpenClaw

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Hermes logo

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CLI logo

CLI

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LangChain

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Mastra AI logo

Mastra AI

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LlamaIndex

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CrewAI logo

CrewAI

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OpenCode logo

OpenCode

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VS Code logo

VS Code

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FAQ

Frequently asked questions

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.

Yes, you can. Vercel AI SDK v6 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.

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.

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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