"Information about Jira project management software" matching MCP servers:

Enables AI assistants to manage project analysis, code metrics, documentation, Git operations, code quality, and file organization through natural language commands.

A Jira Server/Data Center MCP server for issue management, workflow analysis, and field discovery, using JQL and REST API v2 with PAT authentication.

A Model Context Protocol (MCP) server for GitLab and Jira integration. This server allows AI agents like gemini-cli to interact with your GitLab and Jira instances.

A Scrum-aware MCP server that provides semantic analysis of Jira issues, sprint health, and Agile workflow intelligence, returning structured summaries and recommendations instead of raw Jira payloads.

Enables AI agents to manage self-hosted Atlassian Jira Server/Data Center issues and Bitbucket Server/Data Center repositories, pull requests, branches, commits, and files through MCP tools.

An MCP server for end-to-end Jira and GitHub integration, enabling AI agents in the IDE to automate workflows from issue tracking to pull request management.

Enables MCP clients to execute commands, edit files, run Git workflows, and perform validated independent reviews in real development environments through Docker-based runtimes with separated developer and auditor permissions.

MCP server providing tools to read and write Jira Cloud issues, including a bridge to GitHub commits for tracing issues to code.

Enables AI assistants to discover and interact with GitHub repositories through standardized MCP tools, allowing exploration and retrieval of project information without direct API calls.

Provides unified project context and monitoring capabilities including project health metrics, build diagnostics, Git integration, and infrastructure validation to initialize development sessions with comprehensive project information.

An MCP server that reads public GitHub Projects v2 boards (including item-level data like status and custom fields) without authentication, using official APIs and fallback methods for user-owned projects.

A Python framework that exposes developer tools through MCP, REST API, and CLI from a single shared registry, enabling AI assistants, HTTP clients, and terminal users to access the same tools.

Enables AI agents to interact with Jira and GitHub through MCP tools, including searching, creating, commenting on, and transitioning Jira issues and listing pull requests, summarizing diffs, creating issues, and commenting on GitHub.

Provides comprehensive tools for managing GitHub projects, milestones, tasks, and sprints. This server integrates deeply with GitHub Projects V2, offering features like automated kanban workflows, sprint planning, and custom field management.

Enables seamless integration with GitLab and Jira Server/Data Center via MCP, using Bearer token authentication and REST API v2, supporting both on-premise instances.

Enables an AI coding agent to scan a day's git commits across work repositories and group them into Jira tickets, creating or updating them with estimates and logged time. It also surfaces actionable, categorized errors so an agent knows what failed and whether to retry.

Enables end-to-end automation of developer workflows from Jira issue tracking to GitHub pull requests through natural language, allowing developers to search issues, create branches, commit changes, and manage PRs directly from their IDE.

Enables Codex and other MCP clients to read kickoff cards, acceptance criteria, Git evidence, and next steps, then append validated, audited progress events for resumable project handoffs.

Enables AI coding agents and teams to turn GitHub Projects v2 into an agentic task substrate where agents register, claim work atomically, heartbeat, review each other, and track token budgets. It also provides AI-powered PRD generation, task breakdown, sprint planning, issue triage, roadmap creation, and GitHub Actions/Releases/Branches management with no external infrastructure.

A modular MCP server integrating Jira and GitHub to automate developer workflows, enabling AI agents to manage issues, branches, commits, and pull requests directly from the IDE.