Atlassian Cloud

CommunityPopular

sooperset

MCP server for Atlassian tools (Confluence, Jira)

Publisher	sooperset
Repository	`mcp-atlassian`
Language	Python
Forks	1.2K
Stars	5.2K
Available tools	0
Transport type	stdio
Categories	Productivity
License	MIT
Links	GitHub Homepage

Connect tools to AI workflows
Atlassian Cloud exposes MCP capabilities that can be used by compatible AI clients and agents.
0 available tools
Browse the callable actions below, including names and descriptions when provided by the server.
Ready-to-copy setup
Use the installation snippets to configure this server in your preferred MCP client.
Open source signals
5.2K stars and 1.2K forks from the linked repository.

MCP Atlassian

PyPI Version PyPI - Downloads License

Model Context Protocol (MCP) server for Atlassian products (Confluence and Jira). Supports both Cloud and Server/Data Center deployments.

https://github.com/user-attachments/assets/35303504-14c6-4ae4-913b-7c25ea511c3e

https://github.com/user-attachments/assets/7fe9c488-ad0c-4876-9b54-120b666bb785

Quick Start

1. Get Your API Token

Go to https://id.atlassian.com/manage-profile/security/api-tokens and create a token.

For Server/Data Center, use a Personal Access Token instead. See Authentication.

2. Configure Your IDE

Add to your Claude Desktop or Cursor MCP configuration:

json
{
  "mcpServers": {
    "mcp-atlassian": {
      "command": "uvx",
      "args": ["mcp-atlassian"],
      "env": {
        "JIRA_URL": "https://your-company.atlassian.net",
        "JIRA_USERNAME": "your.email@company.com",
        "JIRA_API_TOKEN": "your_api_token",
        "CONFLUENCE_URL": "https://your-company.atlassian.net/wiki",
        "CONFLUENCE_USERNAME": "your.email@company.com",
        "CONFLUENCE_API_TOKEN": "your_api_token"
      }
    }
  }
}

Server/Data Center users: Use JIRA_PERSONAL_TOKEN instead of JIRA_USERNAME + JIRA_API_TOKEN. See Authentication for details.

3. Start Using

Ask your AI assistant to:

"Find issues assigned to me in PROJ project"
"Search Confluence for onboarding docs"
"Create a bug ticket for the login issue"
"Update the status of PROJ-123 to Done"

Documentation

Full documentation is available at mcp-atlassian.soomiles.com.

Documentation is also available in llms.txt format, which LLMs can consume easily:

llms.txt — documentation sitemap
llms-full.txt — complete documentation

Topic	Description
Installation	uvx, Docker, pip, from source
Authentication	API tokens, PAT, OAuth 2.0
Configuration	IDE setup, environment variables
HTTP Transport	SSE, streamable-http, multi-user
Tools Reference	All Jira & Confluence tools
Troubleshooting	Common issues & debugging

Compatibility

Product	Deployment	Support
Confluence	Cloud	Fully supported
Confluence	Server/Data Center	Supported (v6.0+)
Jira	Cloud	Fully supported
Jira	Server/Data Center	Supported (v8.14+)

Key Tools

Jira	Confluence
`jira_search` - Search with JQL	`confluence_search` - Search with CQL
`jira_get_issue` - Get issue details	`confluence_get_page` - Get page content
`jira_create_issue` - Create issues	`confluence_create_page` - Create pages
`jira_update_issue` - Update issues	`confluence_update_page` - Update pages
`jira_transition_issue` - Change status	`confluence_add_comment` - Add comments

72 tools total — See Tools Reference for the complete list.

Security

Never share API tokens. Keep .env files secure. See SECURITY.md.

Contributing

See CONTRIBUTING.md for development setup.

License

MIT - See LICENSE. Not an official Atlassian product.

Installation

TypingMind

Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "mcp-atlassian": {
      "command": "uvx",
      "args": [
        "mcp-atlassian"
      ],
      "env": {
        "CONFLUENCE_URL": "https://your-company.atlassian.net/wiki",
        "CONFLUENCE_USERNAME": "your.email@company.com",
        "CONFLUENCE_API_TOKEN": "your_confluence_api_token",
        "JIRA_URL": "https://your-company.atlassian.net",
        "JIRA_USERNAME": "your.email@company.com",
        "JIRA_API_TOKEN": "your_jira_api_token"
      }
    }
  }
}

Use Atlassian Cloud MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Atlassian Cloud is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

Open TypingMind in your browser.
Click the Settings icon.
Go to Advanced Settings.
Open the Model Context Protocol section.
Click Setup Connector and choose This Device.

TypingMind MCP connector setup screen with This Device selected

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

Copy the setup command shown by TypingMind.
Open Terminal on macOS or Windows Terminal on Windows.
Paste and run the command.
Approve the package install if Terminal asks you to proceed.
Keep the Terminal window running while using MCP tools.

Add Atlassian Cloud as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

Wait until the connector status shows Ready.
Click Edit Servers.
Paste the Atlassian Cloud MCP server configuration.
Save the server list.
Refresh if you want to confirm the connector is still ready.

TypingMind MCP settings showing active server and Edit Servers button

Use it across models

Save the server list, open Plugins, enable the Atlassian Cloud MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

Open the Plugins page in TypingMind.
Enable the Atlassian Cloud MCP tools.
Start a chat and choose the AI model you want to use.
Use the MCP tools in chat or assign them to an AI agent.
Switch to another AI model whenever needed without reconnecting MCP.

TypingMind chat using enabled MCP tools with a selected AI model

Frequently asked questions

What is the Atlassian Cloud MCP server used for?

Atlassian Cloud is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Atlassian Cloud MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Atlassian Cloud with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Atlassian Cloud MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Atlassian Cloud connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Atlassian Cloud MCP to TypingMind?

Atlassian Cloud runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Atlassian Cloud MCP provide in TypingMind?

Atlassian Cloud exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Atlassian Cloud MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Atlassian Cloud requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

Related MCP Servers

View all

GitHub

github

OrganizationPopular

GitHub's official MCP Server

Task Master

An AI-powered task-management system you can drop into Cursor, Lovable, Windsurf, Roo, and others.

Mastra Docs

From the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.

Beads

Beads - A memory upgrade for your coding agent

MCP Atlassian

Quick Start

1. Get Your API Token

2. Configure Your IDE

3. Start Using

Documentation

Compatibility

Key Tools

Security

Contributing

License

Installation

Use Atlassian Cloud MCP with multiple AI models

Setup guide to use the local connector

Open the MCP settings

Run the connector command

Add Atlassian Cloud as a server

Use it across models

Frequently asked questions

Related MCP Servers

Set up your own AI workspace now