# How to integrate Jira MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Jira MCP with Vercel AI SDK v6", "toolkit": "Jira", "toolkit_slug": "jira", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/jira/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/jira/framework/ai-sdk.md", "updated_at": "2026-05-06T08:17:11.418Z" } ``` ## 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 - [ChatGPT](https://composio.dev/toolkits/jira/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/jira/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/jira/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/jira/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/jira/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/jira/framework/codex) - [Cursor](https://composio.dev/toolkits/jira/framework/cursor) - [VS Code](https://composio.dev/toolkits/jira/framework/vscode) - [OpenCode](https://composio.dev/toolkits/jira/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/jira/framework/openclaw) - [Hermes](https://composio.dev/toolkits/jira/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/jira/framework/cli) - [Google ADK](https://composio.dev/toolkits/jira/framework/google-adk) - [LangChain](https://composio.dev/toolkits/jira/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/jira/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/jira/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/jira/framework/crew-ai) ## 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. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `JIRA_ADD_ATTACHMENT` | Add Attachment | Uploads and attaches a file to a jira issue. | | `JIRA_ADD_COMMENT` | Add Comment | Adds a comment using atlassian document format (adf) for rich text to an existing jira issue. | | `JIRA_ADD_WATCHER_TO_ISSUE` | Add Watcher to Issue | Adds a user to an issue's watcher list by account id. | | `JIRA_ASSIGN_ISSUE` | Assign Issue | Assigns a jira issue to a user, default assignee, or unassigns; supports email/name lookup. | | `JIRA_BULK_CREATE_ISSUE` | Bulk Create Issues | Creates multiple jira issues (up to 50 per call) with full feature support including markdown, assignee resolution, and priority handling. | | `JIRA_CREATE_ISSUE` | Create Issue | Creates a new jira issue (e.g., bug, task, story) in a specified project. | | `JIRA_CREATE_ISSUE_LINK` | Link Issues | Links two jira issues using a specified link type with optional comment. | | `JIRA_CREATE_PROJECT` | Create Project | Creates a new jira project with required lead, template, and type configuration. | | `JIRA_CREATE_SPRINT` | Create Sprint | Creates a new sprint on a jira board with optional start/end dates and goal. | | `JIRA_CREATE_VERSION` | Create Version | Creates a new version for releases or milestones in a jira project. | | `JIRA_DELETE_COMMENT` | 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. | | `JIRA_DELETE_ISSUE` | Delete Issue | Deletes a jira issue by its id or key. | | `JIRA_DELETE_VERSION` | Delete Version | Deletes a jira version and optionally reassigns its issues. | | `JIRA_DELETE_WORKLOG` | Delete Worklog | Deletes a worklog from a jira issue with estimate adjustment options. | | `JIRA_EDIT_ISSUE` | Edit Issue | Updates an existing jira issue with field values and operations. supports direct field parameters (summary, description, assignee, priority, etc.) that are merged with the fields parameter. direct parameters take precedence. | | `JIRA_FIND_USERS` | Find Users | Searches for jira users by email, display name, or username to find account ids; essential for assigning issues, adding watchers, and other user-related operations. | | `JIRA_GET_ALL_ISSUE_TYPE_SCHEMES` | Get All Issue Type Schemes | Retrieves all jira issue type schemes with optional filtering and pagination. | | `JIRA_GET_ALL_PROJECTS` | Get all projects | Retrieves all visible projects using the modern paginated jira api with server-side filtering and pagination support. | | `JIRA_GET_ALL_STATUSES` | Get Issue Statuses | Retrieves all available issue statuses from jira with details. | | `JIRA_GET_ALL_USERS` | Get All Users | Retrieves all users from the jira instance including active, inactive, and other user states with pagination support. | | `JIRA_GET_COMMENT` | Get Comment | Retrieves a specific comment by id from a jira issue with optional expansions. | | `JIRA_GET_CURRENT_USER` | Get Current User | Retrieves detailed information about the currently authenticated jira user. | | `JIRA_GET_ISSUE` | Get Issue | Retrieves a jira issue by id or key with customizable fields and expansions. | | `JIRA_GET_ISSUE_LINK_TYPES` | Get Issue Link Types | Retrieves all configured issue link types from jira. | | `JIRA_GET_ISSUE_PROPERTY` | Get Issue Property | Retrieves a custom property from a jira issue by key. | | `JIRA_GET_ISSUE_RESOLUTIONS` | Get Issue Resolutions | Retrieves all available issue resolution types from jira. | | `JIRA_GET_ISSUE_TYPES` | 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. | | `JIRA_GET_ISSUE_TYPE_SCHEME` | Get Issue Type Scheme | Gets a jira issue type scheme by id with all associated issue types. | | `JIRA_GET_ISSUE_WATCHERS` | Get Issue Watchers | Retrieves users watching a jira issue for update notifications. | | `JIRA_GET_ISSUE_WORKLOGS` | Get Issue Worklogs | Retrieves worklogs for a jira issue with user permission checks. | | `JIRA_GET_PROJECT_VERSIONS` | Get Project Versions | Retrieves all versions for a jira project with optional expansion. | | `JIRA_GET_REMOTE_ISSUE_LINKS` | Get Issue Remote Links | Retrieves links from a jira issue to external resources. | | `JIRA_GET_TRANSITIONS` | Get Transitions | Retrieves available workflow transitions for a jira issue. | | `JIRA_GET_VOTES` | Get Issue Votes | Fetches voting details for a jira issue; requires voting to be enabled in jira's general settings. | | `JIRA_GET_WORKLOG` | Get Worklogs | Retrieves worklogs for a specified jira issue. | | `JIRA_LIST_BOARDS` | List Boards | Retrieves paginated jira boards with filtering and sorting options. | | `JIRA_LIST_ISSUE_COMMENTS` | List Issue Comments | Retrieves paginated comments from a jira issue with optional ordering. | | `JIRA_LIST_SPRINTS` | List Sprints | Retrieves paginated sprints from a jira board with optional state filtering. | | `JIRA_MOVE_ISSUE_TO_SPRINT` | Move Issues to Sprint | Moves one or more jira issues to a specified active sprint. | | `JIRA_REMOVE_WATCHER_FROM_ISSUE` | Remove Watcher from Issue | Removes a user from an issue's watcher list by account id. | | `JIRA_SEARCH_FOR_ISSUES_USING_JQL_GET` | Search Issues Using JQL (GET) | Searches for jira issues using jql with pagination and field selection. | | `JIRA_SEARCH_FOR_ISSUES_USING_JQL_POST` | 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 | | `JIRA_SEARCH_ISSUES` | Search issues | Advanced jira issue search supporting structured filters and raw jql. | | `JIRA_SEND_NOTIFICATION_FOR_ISSUE` | Send Notification for Issue | Sends a customized email notification for a jira issue. | | `JIRA_TRANSITION_ISSUE` | Transition Issue | Transitions a jira issue to a different workflow state, with support for transition name lookup and user assignment by email. | | `JIRA_UPDATE_COMMENT` | Update Comment | Updates text content or visibility of an existing jira comment. | ## Supported Triggers | Trigger slug | Name | Description | |---|---|---| | `JIRA_NEW_ISSUE_TRIGGER` | New Issue | Triggered when a new issue is created in Jira | | `JIRA_NEW_PROJECT_TRIGGER` | New Project | Triggered when a new project is added in Jira | | `JIRA_UPDATED_ISSUE_TRIGGER` | Updated Issue | Triggered when an issue is updated in Jira | ## Creating MCP Server - Stand-alone vs Composio SDK The Jira MCP server is an implementation of the Model Context Protocol that connects your AI agent to Jira. It provides structured and secure access so your agent can perform Jira operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before you begin, make sure you have: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install required dependencies 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 ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables 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 ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment 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 ```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, }); ``` ### 5. Create Tool Router session and initialize MCP client 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 ```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; ``` ### 6. Connect to MCP server and retrieve 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 ```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(); ``` ### 7. Initialize conversation and CLI interface 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 ```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(); ``` ### 8. Handle user input and stream responses with real-time tool feedback 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 ```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("\n๐Ÿ‘‹ Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```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("\n๐Ÿ‘‹ 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. ## How to build Jira MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/jira/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/jira/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/jira/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/jira/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/jira/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/jira/framework/codex) - [Cursor](https://composio.dev/toolkits/jira/framework/cursor) - [VS Code](https://composio.dev/toolkits/jira/framework/vscode) - [OpenCode](https://composio.dev/toolkits/jira/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/jira/framework/openclaw) - [Hermes](https://composio.dev/toolkits/jira/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/jira/framework/cli) - [Google ADK](https://composio.dev/toolkits/jira/framework/google-adk) - [LangChain](https://composio.dev/toolkits/jira/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/jira/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/jira/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/jira/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - 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](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - 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](https://composio.dev/toolkits/asana) - 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. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives. - [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions. - [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date. - [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page. - [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes. - [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights. - [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement. - [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management. ## Frequently Asked Questions ### 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 Vercel AI SDK v6? 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. ### 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. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)