Frequently Asked Questions (FAQ) about Gerrit Review MCP Server

Q: What is the Gerrit Review MCP Server? A: The Gerrit Review MCP Server is an integration that connects Gerrit code review system with AI assistants, allowing AI models to review code changes and provide intelligent feedback through a simple interface.

Q: What is MCP? A: MCP stands for Model Context Protocol. It’s an open protocol that standardizes how applications provide context to LLMs, enabling AI models to access and interact with external data sources and tools.

Q: What are the key features of the Gerrit Review MCP Server? A: Key features include fetching change details (including files, patch sets, and diff information), comparing patchset differences, streamlined installation and configuration, and robust troubleshooting.

Q: How does the fetch_gerrit_change function work? A: The fetch_gerrit_change function retrieves complete change information from Gerrit, including project, branch, author, reviewer details, comments, review history, file modifications with diff content, and current patch set information.

Q: What is the purpose of the fetch_patchset_diff function? A: The fetch_patchset_diff function compares differences between two patchsets of a change, allowing you to view specific file differences or all changed files and track the evolution of changes through review iterations.

Q: What are the prerequisites for installing the Gerrit Review MCP Server? A: Prerequisites include Python 3.10 or higher (Python 3.11 recommended), Gerrit HTTP access credentials (username and HTTP password), and access to the mcp[cli] package repository.

Q: How do I install the Gerrit Review MCP Server? A: You can install it either via Smithery using the command npx -y @smithery/cli install @cayirtepeomer/gerrit-code-review-mcp --client claude or manually by cloning the repository, creating a virtual environment, and installing the package using pip install -e ..

Q: How do I configure the Gerrit Review MCP Server? A: Configure the server by setting up environment variables (GERRIT_HOST, GERRIT_USER, GERRIT_HTTP_PASSWORD) or creating a .env file with these variables. Also, generate an HTTP password in Gerrit settings.

Q: How do I generate an HTTP password in Gerrit? A: Log into your Gerrit web interface, go to Settings > HTTP Credentials, and generate a new password. Copy this password to your environment variables or .env file.

Q: How do I configure the MCP server with Cursor? A: Add the server’s configuration to your ~/.cursor/mcp.json file, specifying the path to the Python interpreter, server.py file, working directory, and environment variables.

Q: What should I do if I encounter connection issues? A: Verify your HTTP password, check the GERRIT_HOST setting, ensure HTTPS access is enabled on the Gerrit server, test the connection using curl, and verify Gerrit access permissions for your account.

Q: What is UBOS? A: UBOS is a full-stack AI Agent Development Platform focused on bringing AI Agents to every business department. It helps you orchestrate AI Agents, connect them with enterprise data, build custom AI Agents with your LLM model, and create Multi-Agent Systems.

Q: How does the Gerrit Review MCP Server integrate with UBOS? A: The Gerrit Review MCP Server can be integrated into UBOS AI Agent workflows, allowing you to create AI Agents that automatically review code changes and provide intelligent feedback, improving the overall quality of the codebase within the UBOS platform.