Atlassian MCP Server Integration

What does “Atlassian” MCP Server do?

The Atlassian MCP Server connects AI assistants with Atlassian’s popular project management tools—Jira and Confluence. Acting as a bridge between AI models and these platforms, it enables automated and intelligent workflows for smart project management. By exposing Jira and Confluence’s data and actions via the Model Context Protocol (MCP), this server empowers AI to interact with tasks, tickets, documentation, and project resources. This allows AI-powered assistants to retrieve, update, and manage project information, automate repetitive tasks, and provide contextual insights—streamlining developer and team productivity by embedding AI deeply into project management operations.

List of Prompts

No prompt templates are mentioned in the repository or its documentation.

List of Resources

No explicit MCP resources are documented in the repository or visible documentation.

List of Tools

No explicit tool list is provided in the repository overview or documentation. Code navigation is required for a detailed tool list, but it is not provided in the visible documentation or README.

Use Cases of this MCP Server

• Jira Ticket Management: AI assistants can create, update, and fetch Jira issues, helping developers track bugs, tasks, and feature requests more efficiently.
• Confluence Knowledge Retrieval: Retrieve documentation or meeting notes from Confluence, allowing AI to answer queries or summarize information for teams.
• Automated Project Reporting: Generate and deliver project status reports by aggregating Jira and Confluence data for stakeholders.
• Task Automation: Automate repetitive workflow steps, such as assigning tickets, updating statuses, or creating documentation stubs.
• Contextual Assistance: Provide developers with up-to-date context from project management systems to inform code changes, planning sessions, or reviews.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Open your Windsurf configuration file.
3. Add the Atlassian MCP Server using the following JSON snippet:

{
  "mcpServers": {
    "atlassian": {
      "command": "npx",
      "args": ["@phuc-nt/mcp-atlassian-server@latest"]
    }
  }
}

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking the MCP server status in Windsurf.

Claude

1. Prerequisite: Node.js installed.
2. Locate Claude’s configuration for MCP servers.
3. Add the Atlassian MCP Server configuration:

{
  "mcpServers": {
    "atlassian": {
      "command": "npx",
      "args": ["@phuc-nt/mcp-atlassian-server@latest"]
    }
  }
}

4. Save and restart Claude.
5. Confirm that the server is running by checking the Claude interface.

Cursor

1. Make sure Node.js is available.
2. Edit Cursor’s configuration file for MCP servers.
3. Insert the following:

{
  "mcpServers": {
    "atlassian": {
      "command": "npx",
      "args": ["@phuc-nt/mcp-atlassian-server@latest"]
    }
  }
}

4. Save, then restart Cursor.
5. Check Cursor’s MCP section for the Atlassian server.

Cline

1. Install Node.js if not already present.
2. Access the configuration file for Cline.
3. Add the Atlassian MCP Server entry:

{
  "mcpServers": {
    "atlassian": {
      "command": "npx",
      "args": ["@phuc-nt/mcp-atlassian-server@latest"]
    }
  }
}

4. Save and restart Cline.
5. Validate operation by running a test MCP command.

Securing API Keys (Environment Variables Example):

To securely manage your Atlassian credentials, use environment variables (e.g., in a .env file):

ATLASSIAN_API_KEY=your_api_key_here
JIRA_DOMAIN=your_jira_domain
CONFLUENCE_DOMAIN=your_confluence_domain

Example JSON reference (showing env usage):

{
  "mcpServers": {
    "atlassian": {
      "command": "npx",
      "args": ["@phuc-nt/mcp-atlassian-server@latest"],
      "env": {
        "ATLASSIAN_API_KEY": "${ATLASSIAN_API_KEY}",
        "JIRA_DOMAIN": "${JIRA_DOMAIN}",
        "CONFLUENCE_DOMAIN": "${CONFLUENCE_DOMAIN}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{ “MCP-name”: { “transport”: “streamable_http”, “url”: “https://yourmcpserver.example/pathtothemcp/url" } }

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “MCP-name” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

| Supports Roots | ⛔ | Not mentioned in the documentation |

Based on the available documentation, the Atlassian MCP Server provides core integration with Jira and Confluence but lacks detailed public documentation on prompts, resources, and tools. The existence of a MIT license, setup guidance, and real-world use cases are positives, but the absence of deeper protocol and tool specifics holds back a higher rating.

Our opinion

Overall, this MCP server scores moderately well for basic integration and practical use cases but would benefit from improved documentation on MCP-specific features like prompts, resources, tools, roots, and sampling.

MCP Score