# How to integrate Jira MCP with CrewAI

```json
{
  "title": "How to integrate Jira MCP with CrewAI",
  "toolkit": "Jira",
  "toolkit_slug": "jira",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/jira/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/jira/framework/crew-ai.md",
  "updated_at": "2026-05-06T08:17:11.418Z"
}
```

## 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 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.

## 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)
- [LlamaIndex](https://composio.dev/toolkits/jira/framework/llama-index)

## 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

| 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 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

### 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 dependencies

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

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**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
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

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")
```

### 5. Create a Composio Tool Router session for Jira

**What's happening:**
- You create a Jira only session through Composio
- Composio returns an MCP HTTP URL that exposes Jira tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["jira"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

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

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_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.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["jira"],
)
url = session.mcp.url

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

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

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

## 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

- [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)
- [LlamaIndex](https://composio.dev/toolkits/jira/framework/llama-index)

## 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 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.

---
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