mcp-jira
An async MCP server for JIRA integration, enabling AI assistants to search, create, and manage JIRA issues via JQL and other operations.
README
JIRA MCP Server (Async)
A high-performance, asynchronous Model Context Protocol (MCP) server that integrates with JIRA using stdio transport, allowing AI assistants to:
- Connect to your company's JIRA instance with async operations
- Search for issues using JQL (JIRA Query Language) with concurrent processing
- Get detailed issue information including comments with improved performance
- Track issue relationships (links, parent/child, epics) efficiently
- Create new issues and update existing ones
- View available workflow transitions
🚀 Performance Features
This async implementation provides significant performance improvements over traditional synchronous JIRA clients:
- Concurrent API Calls: Process multiple JIRA requests simultaneously
- Connection Pooling: Efficient HTTP connection management with
aiohttp - Rate Limiting: Built-in throttling to respect JIRA API limits
- Non-blocking I/O: True async operations that don't block the event loop
- Stdio Transport: Optimized for MCP client integration
- Clean Architecture: Focused on essential tools without unnecessary complexity
Performance Comparison
- Synchronous: Traditional blocking operations
- Asynchronous: Non-blocking concurrent operations with connection pooling
Features
This MCP server provides functionality through MCP tools:
MCP Tools
The server exposes the following MCP tools with jira_ prefixes to avoid conflicts with other MCP servers (like GitHub):
| Tool | Description | Parameters |
|---|---|---|
jira_search_issues |
Search for JIRA issues using JQL | jql: JQL query string<br>max_results: Maximum number of results to return |
jira_get_issue_details |
Get detailed information about a specific JIRA issue | issue_key: The JIRA issue key (e.g., "PROJECT-123") |
jira_get_issue_comments |
Get all comments for a specific JIRA issue | issue_key: The JIRA issue key |
jira_get_issue_links |
Get all links for a specific JIRA issue | issue_key: The JIRA issue key |
jira_get_epic_issues |
Get all issues that belong to a specific epic | epic_key: The JIRA epic issue key |
jira_get_subtasks |
Get all subtasks for a specific JIRA issue | issue_key: The parent JIRA issue key |
jira_get_available_transitions |
Lists available workflow transitions for a given Jira issue | issue_key: The JIRA issue key |
jira_create_issue |
Creates a new issue in a specified Jira project | project_key: Key of the project<br>summary: Issue summary<br>description: Issue description<br>issue_type_name: Type of the issue<br>assignee_name: (Optional) Name of the assignee<br>priority_name: (Optional) Name of the priority<br>labels: (Optional) List of labels<br>custom_fields: (Optional) Dictionary of custom fields |
Architecture
The server uses a clean, tool-focused architecture:
- 8 MCP Tools: All essential JIRA operations as simple, focused functions
- No Resources: Simplified design without MCP resources for easier maintenance
- Async Client: High-performance
AsyncJiraClientwith connection pooling - Comprehensive Logging: Detailed logging for monitoring and debugging
This approach provides:
- ✅ Simplicity: Easy to understand and maintain
- ✅ Performance: Async operations with connection pooling
- ✅ Reliability: Focused functionality with comprehensive error handling
- ✅ Flexibility: All essential JIRA operations available through clean tool interfaces
Setup
Prerequisites
- Python 3.13+
- uv package manager
- JIRA API token from your Atlassian account
Installation
-
Clone this repository:
git clone https://github.com/yourusername/mcp-jira.git cd mcp-jira -
Install dependencies:
uv sync -
Create a
.envfile with your JIRA credentials:cp config.env.example .env -
Edit the
.envfile with your JIRA credentials:# JIRA Configuration JIRA_SERVER_URL=https://your-company.atlassian.net JIRA_API_TOKEN=your_api_token_here # Performance Configuration MAX_CONCURRENT_REQUESTS=2 LOG_LEVEL=INFO # Timeouts (in seconds) REQUEST_TIMEOUT=30 CONNECT_TIMEOUT=10
Running the Server
This is a STDIO MCP Server designed to be used with MCP clients like Claude Desktop.
The server is designed to be used with MCP clients. For Claude Desktop:
-
Add to Claude Desktop Configuration:
{ "mcpServers": { "jira": { "command": "python", "args": ["/path/to/your/jira_mcp_server.py"], "env": { "JIRA_SERVER_URL": "https://your-company.atlassian.net", "JIRA_API_TOKEN": "your_api_token_here" } } } } -
Restart Claude Desktop to load the new server configuration.
Environment Variables
The server uses the following environment variables with built-in defaults:
| Variable | Description | Default | Required |
|---|---|---|---|
JIRA_SERVER_URL |
Your JIRA instance URL | None | ✅ Required |
JIRA_API_TOKEN |
Your JIRA API token | None | ✅ Required |
MAX_CONCURRENT_REQUESTS |
Max concurrent requests & rate limit (req/sec) | 2 |
Optional |
REQUEST_TIMEOUT |
HTTP request timeout (seconds) | 30 |
Optional |
CONNECT_TIMEOUT |
HTTP connection timeout (seconds) | 10 |
Optional |
LOG_LEVEL |
Logging level (DEBUG, INFO, WARNING, ERROR) | ERROR |
Optional |
LOG_TO_STDOUT |
Enable stdout logging (interferes with MCP) | false |
Optional |
Only JIRA_SERVER_URL and JIRA_API_TOKEN are required - all other settings have sensible defaults.
Logging
The server includes comprehensive logging:
- Console Output: Real-time status and errors
- Log File: Detailed logs saved to
jira_mcp_server.log - Configurable Levels: Set
LOG_LEVELin your.envfile
Log levels:
DEBUG: Detailed debugging informationINFO: General operational messages (default)WARNING: Warning messages and rate limiting noticesERROR: Error conditions
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.