@judexzhu
Basic information
A high-performance, asynchronous Model Context Protocol (MCP) server that integrates with JIRA using stdio transport
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-jira-judexzhu": {
"command": "uv",
"args": [
"sync"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
A high-performance, asynchronous Model Context Protocol (MCP) server that integrates with JIRA using stdio transport. It allows AI assistants to perform JIRA operations—search, create, update issues, and track relationships—via MCP tools. Designed for developers using MCP clients like Claude Desktop.
Install with uv sync, configure environment variables JIRA_SERVER_URL and JIRA_API_TOKEN, then add the server to your MCP client’s configuration (e.g., Claude Desktop’s mcpServers block). The server runs over stdio and exposes tools with jira_ prefixes.
The server provides tools for searching issues (`jira_search_issues`), getting details (`jira_get_issue_details`), reading comments and links, managing epics/subtasks, viewing transitions, and creating/updating issues—all with a `jira_` tool prefix.
`JIRA_SERVER_URL` (your JIRA instance URL) and `JIRA_API_TOKEN` are required. Optional variables include `MAX_CONCURRENT_REQUESTS` (default 2), `REQUEST_TIMEOUT` (30s), `LOG_LEVEL`, and more.
It uses stdio transport, optimized for MCP client integration. No HTTP endpoints are exposed; the server communicates over standard input/output.
Python 3.13+ and the `uv` package manager. The server uses `aiohttp` for async HTTP calls and implements built-in connection pooling and rate limiting.
Rate limiting is built-in; the default `MAX_CONCURRENT_REQUESTS` value is 2 (requests per second). The server does not expose MCP resources—only tools—and relies entirely on the provided API token for authentication.
🚀 All-in-one MCP server with AI search, RAG, and multi-service integrations (GitLab/Jira/Confluence/YouTube) for AI-enhanced development workflows. Folk from https://github.com/nguyenvanduocit/all-in-one-model-context-protocol
Interact seamlessly with GitLab repositories to manage merge requests and issues. Fetch details, add comments, and streamline your code review process with ease.
Join.cloud lets AI agents work together in real-time rooms. Agents join a room, exchange messages, commit files to shared storage, and optionally review each other's work — all through standard protocols (MCP and A2A).
Model Context Protocol Servers
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