"Tool to analyze backend code, connect with Jira, and automate coding tasks" matching MCP servers:

An MCP server that exposes outputty's task tracker as typed tools backed by GitHub Issues, allowing coding agents to manage tasks with two-way sync.

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

A production-grade MCP server for the Code-Fundi API, enabling AI agents to map codebases, search semantically, and analyze blast radius. It provides tools for repository management, AI-powered research, and impact analysis before shipping changes.

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.

An MCP execution layer that lets capable AI models safely control a developer workstation for file edits, terminal and process execution, Git operations, language-server analysis, browser and Windows UI automation, media processing, job/resource control, and checkpointing. It also coordinates durable single- or multi-agent tasks across providers with leases, dependency graphs, evidence logs, repair loops, and separate verification gates.

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.

Gives any MCP-compatible AI chat or agent a safe, model-neutral coding runtime with file read/search, structured multi-file patches, command execution, interactive sessions, and git operations, all confined to a single workspace and gated by permission modes.

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.

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.

Turns local project directories into persistent MCP workspaces, allowing AI agents to read files, modify code, run commands, manage Git, and save session progress across conversations.

Gives MCP-compatible AI clients safe, hands-on access to local codebases: file reading/search, multi-file patches, command execution, interactive sessions, Git inspection, and coordination of local agent providers such as Antigravity and OpenCode.

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

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.

Enables searching GitHub repositories, fetching readable web page text, and querying a local book database through MCP tool calls.

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.

A local MCP server that gives AI coding assistants read, write, search, and execute capabilities within a specified project directory. This Windows GUI build simplifies setup by eliminating manual Python, Node.js, or Docker installation.

A production-oriented MCP server for coding agents that enables multi-project management through secure file operations, Git integration, and safe command execution. It supports project discovery across multiple root directories and provides robust audit logging with both STDIO and HTTP transport options.

Enables Claude Code to record a verifiable history of code changes with before/after and rationale, and to propose changes that users can accept or discard, all reviewable on a local web timeline.

An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.

Enables AI assistants to interact with Gitea repositories through intelligent tools for issue/PR management, workflow analysis, compliance checking, and content generation, plus 200+ CLI commands for complete CRUD operations.