Atlassian MCP Server Integration

What does “Atlassian” MCP Server do?

The Atlassian MCP (Model Context Protocol) Server acts as a bridge between AI assistants and Atlassian tools such as Confluence and Jira. By connecting large language models to these platforms, the server enables enhanced development workflows, allowing AI agents to interact directly with project management and documentation systems. This integration facilitates tasks like querying issues, managing documentation, and automating repetitive actions within Atlassian environments. The server empowers developers and teams to streamline their software development lifecycle by leveraging AI to automate operations, retrieve relevant context, or perform complex queries across Atlassian products—ultimately boosting productivity and ensuring up-to-date information access.

List of Prompts

No prompt templates were found in the provided repository files or documentation.

List of Resources

No explicit MCP resources are documented or exposed in the available repository files.

List of Tools

No direct listing of tools or tool definitions (e.g., query_database, call_api) could be identified from the available content or directory structure.

Use Cases of this MCP Server

• Project Issue Management
  Integrate with Jira to automatically query, update, or create issues, allowing developers to manage tasks directly from within their AI-powered workflows.

• Automated Documentation Retrieval
  Connect with Confluence to fetch, update, or summarize documentation pages, making it easier to maintain and access up-to-date project information.

• Sprint Planning and Reporting
  Use AI assistants to analyze Jira boards and generate sprint reports or planning documents, reducing manual overhead for project managers.

• Bug Triage and Assignment
  Leverage AI to monitor incoming Jira tickets, suggest possible assignees, and auto-categorize or prioritize issues for faster resolution.

How to set it up

Windsurf

1. Ensure prerequisites such as Node.js and Python are installed.
2. Open your Windsurf configuration file.
3. Add the Atlassian MCP server entry to the mcpServers object with the following JSON snippet:

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

4. Save your configuration and restart Windsurf.
5. Verify that the server is running and accessible.

Securing API Keys

Store your Atlassian API keys in environment variables. Example configuration:

{
  "atlassian": {
    "env": {
      "ATLASSIAN_API_KEY": "your-api-key-here"
    },
    "inputs": {
      "jira_url": "https://your-domain.atlassian.net"
    }
  }
}

Claude

1. Confirm Node.js and Python are installed.
2. Locate Claude’s configuration file.
3. Insert the MCP server details:

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

4. Save and restart Claude.
5. Check the integration via the Claude dashboard.

Securing API Keys

{
  "atlassian": {
    "env": {
      "ATLASSIAN_API_KEY": "your-api-key-here"
    }
  }
}

Cursor

1. Ensure all prerequisites are met (Node.js, etc).
2. Open Cursor’s relevant configuration file.
3. Add:

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

4. Save the file and restart Cursor.
5. Confirm setup through the Cursor interface.

Securing API Keys

{
  "atlassian": {
    "env": {
      "ATLASSIAN_API_KEY": "your-api-key-here"
    }
  }
}

Cline

1. Make sure Node.js is installed.
2. Edit the Cline configuration file.
3. Add:

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

4. Save and restart Cline.
5. Test if the MCP server is reachable.

Securing API Keys

{
  "atlassian": {
    "env": {
      "ATLASSIAN_API_KEY": "your-api-key-here"
    }
  }
}

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:

{
  "atlassian": {
    "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 “atlassian” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above tables, the Atlassian MCP server offers a robust starting point for Atlassian integration, especially given its popularity and open-source license. However, documentation on prompts, explicit resources, and tool definitions is currently lacking, so the server’s discoverability and extensibility could be improved. Overall, it earns a solid score for integration potential and adoption but loses some points for missing detailed MCP-specific documentation.

MCP Score