OpenGrok MCP Server

OpenGrok MCP Server

Code intelligence for any OpenGrok-indexed codebase — search, read, blame, symbol navigation, diffs, commit history, call graphs, dependency maps, and guided investigation. Optimized for token efficiency through Code Mode and AST-aware code reads.

Quick Start

Option 1 — VS Code Extension

Install OpenGrok MCP from the VS Code Marketplace, or search "OpenGrok" in the Extensions panel. The configuration panel opens on first launch — enter your OpenGrok endpoint, username, and password, then click Save Settings and reload when prompted.

The extension provides a visual configuration UI and manages the MCP server process automatically. No Python, external Node.js install, or manual environment setup required.

Option 2 — npm / npx CLI

npm install -g opengrok-mcp-server
opengrok-mcp setup      # interactive wizard: URL, credentials, MCP client registration

Or run without installing:

npx opengrok-mcp-server setup

Other CLI commands:

opengrok-mcp status      # health check: validates connectivity and detects installed MCP clients
opengrok-mcp setup --test                     # test the stored connection without the wizard
opengrok-mcp setup --set contextBudget=generous  # update one stored setting non-interactively
opengrok-mcp export-audit --format json --output audit.jsonl  # export the audit log
opengrok-mcp version     # print version and exit
opengrok-mcp help        # show all commands

Works with any MCP-compatible client (CLI or IDE). See MCP_CLIENTS.md for config format and troubleshooting.

Credentials are stored in the OS keychain (macOS Keychain, Windows Credential Manager, Linux libsecret) with an AES-256-GCM encrypted file fallback for headless environments.

[!TIP]

Automatic Updates — The extension checks GitHub for new releases once per 24 hours and notifies you when one is available. Use OpenGrok: Check for Updates to check on demand.

The Problem

Engineers working in large codebases face a specific gap when using AI coding assistants. The model's context window contains the file currently open, the conversation, and whatever has been manually shared — but a production codebase has structure, history, and cross-module relationships that exist entirely outside that window.

A symbol defined in one module and called from seventy others. A function whose behavior only becomes clear from the three commits that shaped it. An include chain stretching across a dozen directories. A call graph showing which components depend on a service before it gets refactored.

Without access to the code index, the model fills these gaps by guessing: it fabricates file paths, invents function signatures, misattributes changes to authors. The model is not wrong because it is unintelligent — it is wrong because it is isolated.

OpenGrok already solves this for human engineers. It indexes source in dozens of programming languages, maintains a full-text index across committed history, and exposes definition lookups, reference graphs, blame, directory traversal, and file history through a REST API. The problem was that AI tools had no way to reach it.

How It Works

Shortened. The full README is on GitHub.