How to integrate Jira MCP with CrewAI

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Introduction

This guide walks you through connecting Jira to CrewAI 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 CrewAI 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:
  • Get a Composio API key and configure your Jira connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Jira
  • Build a conversational loop where your agent can execute Jira operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Jira connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Jira via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
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 with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Jira MCP URL

Create a Composio Tool Router session for Jira

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["jira"],
)
url = session.mcp.url
What's happening:
  • You create a Jira only session through Composio
  • Composio returns an MCP HTTP URL that exposes Jira tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Jira Assistant",
    goal="Help users interact with Jira through natural language commands",
    backstory=(
        "You are an expert assistant with access to Jira tools. "
        "You can perform various Jira operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Jira MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Jira operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Jira related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_jira_agent.py

Complete Code

Here's the complete code to get you started with Jira and CrewAI:

python
# file: crewai_jira_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Jira session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["jira"],
    )
    url = session.mcp.url

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Jira assistant agent
    toolkit_agent = Agent(
        role="Jira Assistant",
        goal="Help users interact with Jira through natural language commands",
        backstory=(
            "You are an expert assistant with access to Jira tools. "
            "You can perform various Jira operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Jira operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Jira related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Jira through Composio's Tool Router. The agent can perform Jira operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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

Mastra AI

Mastra AI

Use Jira MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Jira MCP with LlamaIndex

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 CrewAI?

Yes, you can. CrewAI 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.

Used by agents from

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