# How to integrate Jira MCP with LlamaIndex ```json { "title": "How to integrate Jira MCP with LlamaIndex", "toolkit": "Jira", "toolkit_slug": "jira", "framework": "LlamaIndex", "framework_slug": "llama-index", "url": "https://composio.dev/toolkits/jira/framework/llama-index", "markdown_url": "https://composio.dev/toolkits/jira/framework/llama-index.md", "updated_at": "2026-05-06T08:17:11.418Z" } ``` ## Introduction This guide walks you through connecting Jira to LlamaIndex 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 LlamaIndex 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) - [Vercel AI SDK](https://composio.dev/toolkits/jira/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/jira/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/jira/framework/crew-ai) ## TL;DR Here's what you'll learn: - Set your OpenAI and Composio API keys - Install LlamaIndex and Composio packages - Create a Composio Tool Router session for Jira - Connect LlamaIndex to the Jira MCP server - Build a Jira-powered agent using LlamaIndex - Interact with Jira through natural language ## What is LlamaIndex? LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools. Key features include: - ReAct Agent: Reasoning and acting pattern for tool-using agents - MCP Tools: Native support for Model Context Protocol - Context Management: Maintain conversation context across interactions - Async Support: Built for async/await patterns ## 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: - Python 3.8/Node 16 or higher installed - A Composio account with the API key - An OpenAI API key - A Jira account and project - Basic familiarity with async Python/Typescript ### 1. Getting API Keys for OpenAI, Composio, and Jira No description provided. ### 2. Installing dependencies No description provided. ```python pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv ``` ```typescript npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv ``` ### 3. Set environment variables Create a .env file in your project root: These credentials will be used to: - Authenticate with OpenAI's GPT-5 model - Connect to Composio's Tool Router - Identify your Composio user session for Jira access ```bash OPENAI_API_KEY=your-openai-api-key COMPOSIO_API_KEY=your-composio-api-key COMPOSIO_USER_ID=your-user-id ``` ### 4. Import modules No description provided. ```python import asyncio import os import dotenv from composio import Composio from composio_llamaindex import LlamaIndexProvider from llama_index.core.agent.workflow import ReActAgent from llama_index.core.workflow import Context from llama_index.llms.openai import OpenAI from llama_index.tools.mcp import BasicMCPClient, McpToolSpec dotenv.load_dotenv() ``` ```typescript import "dotenv/config"; import readline from "node:readline/promises"; import { stdin as input, stdout as output } from "node:process"; import { Composio } from "@composio/core"; import { mcp } from "@llamaindex/tools"; import { agent as createAgent } from "@llamaindex/workflow"; import { openai } from "@llamaindex/openai"; dotenv.config(); ``` ### 5. Load environment variables and initialize Composio No description provided. ```python OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not OPENAI_API_KEY: raise ValueError("OPENAI_API_KEY is not set in the environment") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment") ``` ```typescript const OPENAI_API_KEY = process.env.OPENAI_API_KEY; const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY; const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID; if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set"); if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set"); ``` ### 6. Create a Tool Router session and build the agent function What's happening here: - We create a Composio client using your API key and configure it with the LlamaIndex provider - We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, jira) - The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools - LlamaIndex will connect to this endpoint to dynamically discover and use the available Jira tools. - The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent. ```python async def build_agent() -> ReActAgent: composio_client = Composio( api_key=COMPOSIO_API_KEY, provider=LlamaIndexProvider(), ) session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["jira"], ) mcp_url = session.mcp.url print(f"Composio MCP URL: {mcp_url}") mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY}) mcp_tool_spec = McpToolSpec(client=mcp_client) tools = await mcp_tool_spec.to_tool_list_async() llm = OpenAI(model="gpt-5") description = "An agent that uses Composio Tool Router MCP tools to perform Jira actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Jira actions. """ return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True) ``` ```typescript async function buildAgent() { console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`); console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`); const composio = new Composio({ apiKey: COMPOSIO_API_KEY, provider: new LlamaindexProvider(), }); const session = await composio.create( COMPOSIO_USER_ID!, { toolkits: ["jira"], }, ); const mcpUrl = session.mcp.url; console.log(`Composio Tool Router MCP URL: ${mcpUrl}`); const server = mcp({ url: mcpUrl, clientName: "composio_tool_router_with_llamaindex", requestInit: { headers: { "x-api-key": COMPOSIO_API_KEY!, }, }, // verbose: true, }); const tools = await server.tools(); const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" }); const agent = createAgent({ name: "composio_tool_router_with_llamaindex", description : "An agent that uses Composio Tool Router MCP tools to perform actions.", systemPrompt: "You are a helpful assistant connected to Composio Tool Router."+ "Use the available tools to answer user queries and perform Jira actions." , llm, tools, }); return agent; } ``` ### 7. Create an interactive chat loop No description provided. ```python async def chat_loop(agent: ReActAgent) -> None: ctx = Context(agent) print("Type 'quit', 'exit', or Ctrl+C to stop.") while True: try: user_input = input("\nYou: ").strip() except (KeyboardInterrupt, EOFError): print("\nBye!") break if not user_input or user_input.lower() in {"quit", "exit"}: print("Bye!") break try: print("Agent: ", end="", flush=True) handler = agent.run(user_input, ctx=ctx) async for event in handler.stream_events(): # Stream token-by-token from LLM responses if hasattr(event, "delta") and event.delta: print(event.delta, end="", flush=True) # Show tool calls as they happen elif hasattr(event, "tool_name"): print(f"\n[Using tool: {event.tool_name}]", flush=True) # Get final response response = await handler print() # Newline after streaming except KeyboardInterrupt: print("\n[Interrupted]") continue except Exception as e: print(f"\nError: {e}") ``` ```typescript async function chatLoop(agent: ReturnType) { const rl = readline.createInterface({ input, output }); console.log("Type 'quit' or 'exit' to stop."); while (true) { let userInput: string; try { userInput = (await rl.question("\nYou: ")).trim(); } catch { console.log("\nAgent: Bye!"); break; } if (!userInput) { continue; } const lower = userInput.toLowerCase(); if (lower === "quit" || lower === "exit") { console.log("Agent: Bye!"); break; } try { process.stdout.write("Agent: "); const stream = agent.runStream(userInput); let finalResult: any = null; for await (const event of stream) { // The event.data contains the streamed content const data: any = event.data; // Check for streaming delta content if (data?.delta) { process.stdout.write(data.delta); } // Store final result for fallback if (data?.result || data?.message) { finalResult = data; } } // If no streaming happened, show the final result if (finalResult) { const answer = finalResult.result ?? finalResult.message?.content ?? finalResult.message ?? ""; if (answer && typeof answer === "string" && !answer.includes("[object")) { process.stdout.write(answer); } } console.log(); // New line after streaming completes } catch (err: any) { console.error("\nAgent error:", err?.message ?? err); } } rl.close(); } ``` ### 8. Define the main entry point What's happening here: - We're orchestrating the entire application flow - The agent gets built with proper error handling - Then we kick off the interactive chat loop so you can start talking to Jira ```python async def main() -> None: agent = await build_agent() await chat_loop(agent) if __name__ == "__main__": # Handle Ctrl+C gracefully signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0))) try: asyncio.run(main()) except KeyboardInterrupt: print("\nBye!") ``` ```typescript async function main() { try { const agent = await buildAgent(); await chatLoop(agent); } catch (err) { console.error("Failed to start agent:", err); process.exit(1); } } main(); ``` ### 9. Run the agent When prompted, authenticate and authorise your agent with Jira, then start asking questions. ```bash python llamaindex_agent.py ``` ```typescript npx ts-node llamaindex-agent.ts ``` ## Complete Code ```python import asyncio import os import signal import dotenv from composio import Composio from composio_llamaindex import LlamaIndexProvider from llama_index.core.agent.workflow import ReActAgent from llama_index.core.workflow import Context from llama_index.llms.openai import OpenAI from llama_index.tools.mcp import BasicMCPClient, McpToolSpec dotenv.load_dotenv() OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not OPENAI_API_KEY: raise ValueError("OPENAI_API_KEY is not set") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") async def build_agent() -> ReActAgent: composio_client = Composio( api_key=COMPOSIO_API_KEY, provider=LlamaIndexProvider(), ) session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["jira"], ) mcp_url = session.mcp.url print(f"Composio MCP URL: {mcp_url}") mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY}) mcp_tool_spec = McpToolSpec(client=mcp_client) tools = await mcp_tool_spec.to_tool_list_async() llm = OpenAI(model="gpt-5") description = "An agent that uses Composio Tool Router MCP tools to perform Jira actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Jira actions. """ return ReActAgent( tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True, ); async def chat_loop(agent: ReActAgent) -> None: ctx = Context(agent) print("Type 'quit', 'exit', or Ctrl+C to stop.") while True: try: user_input = input("\nYou: ").strip() except (KeyboardInterrupt, EOFError): print("\nBye!") break if not user_input or user_input.lower() in {"quit", "exit"}: print("Bye!") break try: print("Agent: ", end="", flush=True) handler = agent.run(user_input, ctx=ctx) async for event in handler.stream_events(): # Stream token-by-token from LLM responses if hasattr(event, "delta") and event.delta: print(event.delta, end="", flush=True) # Show tool calls as they happen elif hasattr(event, "tool_name"): print(f"\n[Using tool: {event.tool_name}]", flush=True) # Get final response response = await handler print() # Newline after streaming except KeyboardInterrupt: print("\n[Interrupted]") continue except Exception as e: print(f"\nError: {e}") async def main() -> None: agent = await build_agent() await chat_loop(agent) if __name__ == "__main__": # Handle Ctrl+C gracefully signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0))) try: asyncio.run(main()) except KeyboardInterrupt: print("\nBye!") ``` ```typescript import "dotenv/config"; import readline from "node:readline/promises"; import { stdin as input, stdout as output } from "node:process"; import { Composio } from "@composio/core"; import { LlamaindexProvider } from "@composio/llamaindex"; import { mcp } from "@llamaindex/tools"; import { agent as createAgent } from "@llamaindex/workflow"; import { openai } from "@llamaindex/openai"; dotenv.config(); const OPENAI_API_KEY = process.env.OPENAI_API_KEY; const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY; const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID; if (!OPENAI_API_KEY) { throw new Error("OPENAI_API_KEY is not set in the environment"); } if (!COMPOSIO_API_KEY) { throw new Error("COMPOSIO_API_KEY is not set in the environment"); } if (!COMPOSIO_USER_ID) { throw new Error("COMPOSIO_USER_ID is not set in the environment"); } async function buildAgent() { console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`); console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`); const composio = new Composio({ apiKey: COMPOSIO_API_KEY, provider: new LlamaindexProvider(), }); const session = await composio.create( COMPOSIO_USER_ID!, { toolkits: ["jira"], }, ); const mcpUrl = session.mcp.url; console.log(`Composio Tool Router MCP URL: ${mcpUrl}`); const server = mcp({ url: mcpUrl, clientName: "composio_tool_router_with_llamaindex", requestInit: { headers: { "x-api-key": COMPOSIO_API_KEY!, }, }, // verbose: true, }); const tools = await server.tools(); const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" }); const agent = createAgent({ name: "composio_tool_router_with_llamaindex", description: "An agent that uses Composio Tool Router MCP tools to perform actions.", systemPrompt: "You are a helpful assistant connected to Composio Tool Router."+ "Use the available tools to answer user queries and perform Jira actions." , llm, tools, }); return agent; } async function chatLoop(agent: ReturnType) { const rl = readline.createInterface({ input, output }); console.log("Type 'quit' or 'exit' to stop."); while (true) { let userInput: string; try { userInput = (await rl.question("\nYou: ")).trim(); } catch { console.log("\nAgent: Bye!"); break; } if (!userInput) { continue; } const lower = userInput.toLowerCase(); if (lower === "quit" || lower === "exit") { console.log("Agent: Bye!"); break; } try { process.stdout.write("Agent: "); const stream = agent.runStream(userInput); let finalResult: any = null; for await (const event of stream) { // The event.data contains the streamed content const data: any = event.data; // Check for streaming delta content if (data?.delta) { process.stdout.write(data.delta); } // Store final result for fallback if (data?.result || data?.message) { finalResult = data; } } // If no streaming happened, show the final result if (finalResult) { const answer = finalResult.result ?? finalResult.message?.content ?? finalResult.message ?? ""; if (answer && typeof answer === "string" && !answer.includes("[object")) { process.stdout.write(answer); } } console.log(); // New line after streaming completes } catch (err: any) { console.error("\nAgent error:", err?.message ?? err); } } rl.close(); } async function main() { try { const agent = await buildAgent(); await chatLoop(agent); } catch (err: any) { console.error("Failed to start agent:", err?.message ?? err); process.exit(1); } } main(); ``` ## Conclusion You've successfully connected Jira to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways: - Tool Router dynamically exposes Jira tools through an MCP endpoint - LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations - The agent becomes more capable without increasing prompt size - Async Python provides clean, efficient execution of agent workflows You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter. ## 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) - [Vercel AI SDK](https://composio.dev/toolkits/jira/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/jira/framework/mastra-ai) - [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 LlamaIndex? Yes, you can. LlamaIndex 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)