Jira CLI MCP

A Model Context Protocol (MCP) server that wraps the Jira CLI tool, exposing Jira commands through MCP resources, tools, and prompts for AI assistant integration.

Features

• MCP Resources: Access Jira data through standardized URL endpoints
• MCP Tools: Execute Jira commands (create issues, edit, assign, etc.)
• MCP Prompts: Use templates for common Jira workflows
• Authentication: Uses existing Jira CLI authentication
• Project Configuration: Customize default project and config

Prerequisites

• Python 3.11+
• Jira CLI installed and configured
• Access to a Jira instance

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/jira-cli-mcp.git
   cd jira-cli-mcp

1. Install dependencies with uv:

   uv sync
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Configuration

The server can be configured via environment variables:

• JIRA_CONFIG_FILE: Path to a custom Jira CLI config file
• JIRA_PROJECT: Default Jira project to use

Example:

export JIRA_PROJECT="PROJ"
export JIRA_CONFIG_FILE="/path/to/config.yml"

Usage

Starting the Server

python main.py

By default, the server will start on port 8080.

Available Resources

• jira://issues - List recent issues
• jira://issue/{issue_key} - View issue details
• jira://epics - List epics
• jira://sprints - List sprints
• jira://projects - List projects
• jira://boards - List boards
• jira://search/{jql} - Search issues with JQL

Available Tools

• create_issue - Create a new Jira issue
• edit_issue - Edit an existing Jira issue
• assign_issue - Assign issue to a user
• move_issue - Move/transition issue to new status
• add_comment - Add comment to issue
• search_issues_tool - Search issues with flexible criteria
• clone_issue - Clone an existing issue
• link_issues - Link two issues
• create_epic - Create a new epic
• add_to_sprint - Add issues to sprint

Available Prompts

• create_bug_report - Template for creating a bug report
• create_feature_request - Template for creating a feature request
• daily_standup_search - Search for issues relevant to daily standup
• issue_triage_workflow - Workflow for triaging new issues

Integrating with AI Assistants

This MCP server can be integrated with AI assistants that support the Model Context Protocol. Example integration:

from mcp.client import MCPClient

# Connect to the MCP server
client = MCPClient("http://localhost:8080")

# Use resources
issues = client.fetch_resource("jira://issues")

# Execute tools
result = client.execute_tool("create_issue", {
    "summary": "Fix login bug",
    "issue_type": "Bug",
    "priority": "High"
})

# Get prompts
template = client.get_prompt("create_bug_report", {
    "component": "Authentication", 
    "severity": "High"
})

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.