MCP Jira Integration
About
JIRA integration server for Model Context Protocol (MCP) - enables LLMs to interact with JIRA tasks and workflows
Details
- License
- MIT
Explore
- Multi-Project Support: Work with multiple projects by specifying project keys dynamically
- Sprint progress tracking with visual indicators
- Team workload analysis and capacity planning
- Automated daily standup report generation
- Issue creation with proper prioritization
- Smart search and filtering of issues
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP Jira IntegrationCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
- Python 3.8 or higher
- Jira Cloud account with API token
- MCP-compatible client (like Claude Desktop)
1. Clone and install:
git clone https://github.com/your-org/mcp-jira.git
cd mcp-jira
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
2. Configure Jira credentials:
``bash
cp .env.example .env
- JIRA_URL - Your Jira instance URLJIRA_USERNAME
- - Your Jira username/emailJIRA_API_TOKEN
- - Your Jira API tokenPROJECT_KEY` - Default project key for operations (can be overridden per request)
-
1. Go to Atlassian Account Settings
2. Click "Create API token"
3. Give it a name and copy the token
4. Use your email as username and the token as password
- create_issue - Create new Jira issues with proper formatting and ADF descriptions
- search_issues - Search issues using JQL with smart formatting and pagination
- get_sprint_status - Get comprehensive sprint progress reports with metrics
- get_team_workload - Analyze team member workloads and capacity
- generate_standup_report - Generate daily standup reports automatically
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp jira integration": {
"mcp-jira-warzuponus": {
"command": "python3",
"args": [
"-m",
"venv",
".venv"
]
}
}
}
}McpServers
{
"mcp-jira-warzuponus": {
"command": "python3",
"args": [
"-m",
"venv",
".venv"
]
}
}A simple Model Context Protocol (MCP) server for Jira that allows LLMs to act as project managers and personal assistants for teams using Jira. Built on the Jira REST API v3.
Features
Core MCP Tools
- create_issue - Create new Jira issues with proper formatting and ADF descriptions - search_issues - Search issues using JQL with smart formatting and pagination - get_sprint_status - Get comprehensive sprint progress reports with metrics - get_team_workload - Analyze team member workloads and capacity - generate_standup_report - Generate daily standup reports automaticallyProject Management Capabilities
- Multi-Project Support: Work with multiple projects by specifying project keys dynamically - Sprint progress tracking with visual indicators - Team workload analysis and capacity planning - Automated daily standup report generation - Issue creation with proper prioritization - Smart search and filtering of issuesReliability
- Automatic retry with exponential backoff on rate limits (429) and transient errors (503) - Pagination for large result sets - Timezone-aware date handling - Graceful handling of custom Jira statuses and issue typesRequirements
- Python 3.8 or higher
- Jira Cloud account with API token
- MCP-compatible client (like Claude Desktop)
Quick Setup
1. Clone and install:
git clone https://github.com/your-org/mcp-jira.git
cd mcp-jira
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
2. Configure Jira credentials:
```bash
cp .env.example .env
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