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jira-mcp

MCP.Pizza Chef: cosmix

The jira-mcp server is a Model Context Protocol server designed to expose Jira data to AI models efficiently. It supports both Jira Cloud and Jira Server (Data Center) environments, automatically adapting API versions and authentication methods. Key features include searching Jira issues via JQL, retrieving epic children with comment histories, and delivering detailed issue data with related issues. The server optimizes data payloads and cleans data to fit AI context windows, enabling seamless integration of Jira project management data into AI workflows and copilots.

Use This MCP server To

Search Jira issues using JQL queries Retrieve epic children with comments for context Access detailed issue information including related issues Integrate Jira data into AI-powered project management tools Provide real-time Jira context to AI copilots Optimize Jira data payloads for AI consumption Support both Jira Cloud and Server instances

README

JIRA MCP Server

A Model Context Protocol (MCP) server implementation that provides access to JIRA data with relationship tracking, optimized data payloads, and data cleaning for AI context windows.

ℹ️ There is a separate MCP server for Confluence


Jira Cloud & Jira Server (Data Center) Support

This MCP server supports both Jira Cloud and Jira Server (Data Center) instances. You can select which type to use by setting the JIRA_TYPE environment variable:

  • cloud (default): For Jira Cloud (Atlassian-hosted)
  • server: For Jira Server/Data Center (self-hosted)

The server will automatically use the correct API version and authentication method for the selected type.


Features

  • Search JIRA issues using JQL (maximum 50 results per request)
  • Retrieve epic children with comment history and optimized payloads (maximum 100 issues per request)
  • Get detailed issue information including comments and related issues
  • Create, update, and manage JIRA issues
  • Add comments to issues
  • Extract issue mentions from Atlassian Document Format
  • Track issue relationships (mentions, links, parent/child, epics)
  • Clean and transform rich JIRA content for AI context efficiency
  • Support for file attachments with secure multipart upload handling
  • Supports both Jira Cloud and Jira Server (Data Center) APIs

Prerequisites

  • Bun (v1.0.0 or higher)
  • JIRA account with API access

Environment Variables

JIRA_API_TOKEN=your_api_token
JIRA_BASE_URL=your_jira_instance_url  # e.g., https://your-domain.atlassian.net
JIRA_USER_EMAIL=your_email
JIRA_TYPE=cloud   # or 'server' for Jira Server/Data Center (optional, defaults to 'cloud')

Installation & Setup

1. Clone the repository:

git clone [repository-url]
cd jira-mcp

2. Install dependencies and build:

bun install
bun run build

3. Configure the MCP server:

Edit the appropriate configuration file:

macOS:

  • Cline: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
  • Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows:

  • Cline: %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
  • Claude Desktop: %APPDATA%\Claude Desktop\claude_desktop_config.json

Linux:

  • Cline: ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
  • Claude Desktop: sadly doesn't exist yet

Add the following configuration under the mcpServers object:

{
  "mcpServers": {
    "jira": {
      "command": "node",
      "args": ["/absolute/path/to/jira-mcp/build/index.js"],
      "env": {
        "JIRA_API_TOKEN": "your_api_token",
        "JIRA_BASE_URL": "your_jira_instance_url",
        "JIRA_USER_EMAIL": "your_email",
        "JIRA_TYPE": "cloud"
      }
    }
  }
}

4. Restart the MCP server.

Within Cline's MCP settings, restart the MCP server. Restart Claude Desktop to load the new MCP server.

Development

Run tests:

bun test

Watch mode for development:

bun run dev

To rebuild after changes:

bun run build

Available MCP Tools

search_issues

Search JIRA issues using JQL. Returns up to 50 results per request.

Input Schema:

{
  searchString: string; // JQL search string
}

get_epic_children

Get all child issues in an epic including their comments and relationship data. Limited to 100 issues per request.

Input Schema:

{
  epicKey: string; // The key of the epic issue
}

get_issue

Get detailed information about a specific JIRA issue including comments and all relationships.

Input Schema:

{
  issueId: string; // The ID or key of the JIRA issue
}

create_issue

Create a new JIRA issue with specified fields.

Input Schema:

{
  projectKey: string, // The project key where the issue will be created
  issueType: string, // The type of issue (e.g., "Bug", "Story", "Task")
  summary: string, // The issue summary/title
  description?: string, // Optional issue description
  fields?: { // Optional additional fields
    [key: string]: any
  }
}

update_issue

Update fields of an existing JIRA issue.

Input Schema:

{
  issueKey: string, // The key of the issue to update
  fields: { // Fields to update
    [key: string]: any
  }
}

add_attachment

Add a file attachment to a JIRA issue.

Input Schema:

{
  issueKey: string, // The key of the issue
  fileContent: string, // Base64 encoded file content
  filename: string // Name of the file to be attached
}

add_comment

Add a comment to a JIRA issue. Accepts plain text and converts it to the required Atlassian Document Format internally.

Input Schema:

{
  issueIdOrKey: string, // The ID or key of the issue to add the comment to
  body: string // The content of the comment (plain text)
}

Data Cleaning Features

  • Extracts text from Atlassian Document Format
  • Tracks issue mentions in descriptions and comments
  • Maintains formal issue links with relationship types
  • Preserves parent/child relationships
  • Tracks epic associations
  • Includes comment history with author information
  • Removes unnecessary metadata from responses
  • Recursively processes content nodes for mentions
  • Deduplicates issue mentions

Technical Details

  • Built with TypeScript in strict mode
  • Uses Bun runtime for improved performance
  • Vite for optimized builds
  • Uses JIRA REST API v3
  • Basic authentication with API tokens
  • Batched API requests for related data
  • Optimized response payloads for AI context windows
  • Efficient transformation of complex Atlassian structures
  • Robust error handling
  • Rate limiting considerations
  • Maximum limits:
    • Search results: 50 issues per request
    • Epic children: 100 issues per request
  • Support for multipart form data for secure file attachments
  • Automatic content type detection and validation

Error Handling

The server implements a comprehensive error handling strategy:

  • Network error detection and appropriate messaging
  • HTTP status code handling (especially 404 for issues)
  • Detailed error messages with status codes
  • Error details logging to console
  • Input validation for all parameters
  • Safe error propagation through MCP protocol
  • Specialized handling for common JIRA API errors
  • Base64 validation for attachments
  • Multipart request failure handling
  • Rate limit detection
  • Attachment parameter validation

LICENCE

This project is licensed under the MIT License - see the LICENCE file for details.

jira-mcp FAQ

How do I configure jira-mcp for Jira Cloud or Server?
Set the JIRA_TYPE environment variable to 'cloud' for Jira Cloud or 'server' for Jira Server/Data Center. The server auto-selects the correct API and authentication.
What is the maximum number of Jira issues returned per search request?
The server returns up to 50 issues per JQL search request to optimize performance and context window usage.
Can jira-mcp retrieve comments and related issues?
Yes, it provides detailed issue information including comment history and related issues for comprehensive context.
Does jira-mcp support Jira Server (Data Center) instances?
Yes, it supports both Jira Cloud and Jira Server/Data Center with appropriate API handling.
How does jira-mcp optimize data for AI context windows?
It cleans and structures Jira data payloads to fit within AI model context limits, improving efficiency and relevance.
Is authentication handled automatically?
Yes, jira-mcp automatically manages authentication based on the Jira instance type configured.
Where can I find a similar MCP server for Confluence?
There is a separate MCP server for Confluence available at https://github.com/cosmix/confluence-mcp.