"How to review merge requests in GitLab" matching MCP servers:

Enables an assistant to work inside gitlab.com projects as the signed-in user: issues, merge requests, code reviews, repositories, CI pipelines and job logs, plus optional gated toolsets for wiki, snippets, releases, planning, deployments and activity. It authenticates through a user-supplied OAuth application with the token kept in the OS keyring, and registers only the tools that are switched on.

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Model Context Protocol (MCP) server for GitLab — exposes 1006 GitLab REST & GraphQL API operations as MCP tools (42 meta-tools / 57 enterprise), 24 resources, 38 prompts, and 17 completion types for AI assistants. Written in Go, single static binary, stdio and HTTP transport.

Enables natural-language interaction with self-hosted Jira and Bitbucket, providing tools for ticket management, pull requests, code review, and git context.

Gemini-powered code review. Offers review-only and review-and-commit modes with integrated Gitleaks security scanning.

A Bitbucket CLI and MCP server written in Go for managing workspaces, repositories, pull requests, pipelines, issues, and source code. Supports stdio and HTTP transport.

LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.

Enables interaction with Bitbucket through the Model Context Protocol, allowing users to manage pull requests, add comments, review code, create tasks, and perform other repository operations using natural language.

Enables analyzing sales and stock CSV files to identify what to reorder, how much to buy, when products will run out, and how much inventory is tied up in dead stock. Provides read-only inventory checks and CSV column inspection without uploading data.

Enables AI assistants to read, search, organize, and draft email, manage calendar items, and look up contacts through the user's already signed-in classic Outlook desktop app on Windows, with outgoing actions defaulting to drafts for review.

Records how any command-line tool behaves (flags, stdin, outputs, exit codes, edge cases) as golden tests, then diffs a Rust or Go rewrite against the original to prove it behaves the same.

Enables coding agents to debug backend requests by providing tools to list, view, wait for, diagnose, and clear request traces.

An MCP server that exposes Gerrit code review operations as capability-gated tools, enabling AI agents to search and read changes, publish comments, vote, and manage change state, with write capabilities as explicit opt-in.

The Multi DB MCP Server is a high-performance implementation of the Database Model Context Protocol designed to revolutionize how AI agents interact with databases. Currently supporting MySQL and PostgreSQL databases.

YouTrack MCP server and coding-agent pipelines. Every built-in YouTrack MCP tool plus deletes, sprints, attachments, history and reports, signed in with your login and password. Pipelines hand YouTrack issues to Claude Code, Kimi or any ACP agent and open the pull requests. One-command install into Claude, Cursor, VS Code, Zed, Codex and more.

Interact with OneDev for CI/CD spec editing, pull request review, and issue workflow automation etc.

A local stdio MCP server that lets agents use temporary secrets through a private browser form and approved API requests without receiving secret values in tool output. Install the binary for your platform and run secrethandoff mcp; secrets remain in process memory until forgotten or the process exits.

A Model Context Protocol server that connects AI coding agents to Gerrit code review, enabling change search, diff review, and comment publishing without leaving the session.

Lets a human review what an AI agent is about to post to a chat tool (Slack first) before it is sent. Renders the draft, destination and warnings (@here/@channel, unresolved mentions, format issues) in a neutral local HTML preview; never sends anything.

Summarizes GitHub discussions, issues, and pull requests by storing data locally for fast, token-efficient responses.