gitlab-mcp

gitlab-mcp

An MCP server that enables AI agents to perform automated code reviews on GitLab merge requests, including fetching details, reading diffs, posting inline comments, managing labels, and approving or unapproving MRs.

Category
Visit Server

README

gitlab-mcp

<!-- CI badge: update the URL once the repo is pushed to GitHub --> <!-- CI -->

An MCP (Model Context Protocol) server for AI-powered GitLab merge request code reviews.

Provides 23 tools that give an AI agent everything it needs to perform thorough, structured code reviews: fetch MR details, read diffs and files, post inline comments and suggestions, manage labels, track review sessions with re-review intelligence, and approve/unapprove merge requests.

Prerequisites

  • Bun >= 1.0
  • A GitLab Personal Access Token (PAT) or OAuth token with api scope

Quick Start

# Clone and install
git clone <your-repo-url>
cd gitlab-mcp
bun install

# Configure authentication
cp .env.example .env
# Edit .env with your GitLab PAT (see "Creating Tokens" below)

# Run the server (stdio transport)
bun run start

Note: The .env file is automatically loaded when you run bun run start or bun run dev directly. When the server is spawned as a subprocess by an MCP client (Claude Code, OpenCode, etc.), environment variables must be passed through the client's configuration — see the setup examples below.

MCP Client Setup

Connect this server to your AI coding agent. The server uses stdio transport — it communicates over stdin/stdout.

Claude Code

Add via the CLI:

claude mcp add gitlab-mr-review --transport stdio \
  --env GITLAB_PAT=glpat-xxxxxxxxxxxxxxxxxxxx \
  --env GITLAB_BASE_URL=https://gitlab.com \
  --env GITLAB_PROJECT_ID=my-group/my-project \
  -- bun run start

Or to share with your team, add a .mcp.json file to the project root (use --scope project):

claude mcp add gitlab-mr-review --transport stdio --scope project \
  --env GITLAB_PAT=glpat-xxxxxxxxxxxxxxxxxxxx \
  --env GITLAB_BASE_URL=https://gitlab.com \
  --env GITLAB_PROJECT_ID=my-group/my-project \
  -- bun run start

This creates a .mcp.json that can be committed to version control:

{
  "mcpServers": {
    "gitlab-mr-review": {
      "command": "bun",
      "args": ["run", "start"],
      "env": {
        "GITLAB_PAT": "${GITLAB_PAT}",
        "GITLAB_BASE_URL": "${GITLAB_BASE_URL:-https://gitlab.com}",
        "GITLAB_PROJECT_ID": "${GITLAB_PROJECT_ID}"
      }
    }
  }
}

Tip: Claude Code supports ${VAR} and ${VAR:-default} syntax in .mcp.json, so you can reference environment variables instead of hardcoding secrets.

OpenCode

Add the server to your opencode.json (or opencode.jsonc):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "gitlab-mr-review": {
      "type": "local",
      "command": ["bun", "run", "start"],
      "enabled": true,
      "environment": {
        "GITLAB_PAT": "{env:GITLAB_PAT}",
        "GITLAB_BASE_URL": "{env:GITLAB_BASE_URL}",
        "GITLAB_PROJECT_ID": "{env:GITLAB_PROJECT_ID}"
      }
    }
  }
}

Tip: OpenCode uses {env:VAR} syntax to reference environment variables. Set GITLAB_PAT, GITLAB_BASE_URL, and GITLAB_PROJECT_ID in your shell environment or .env file.

Environment Variables

Both clients need these environment variables to be available:

Variable Required Description
GITLAB_PAT Yes* GitLab Personal Access Token (glpat-...)
GITLAB_OAUTH_TOKEN Yes* OAuth token (alternative to PAT)
GITLAB_BASE_URL No GitLab instance URL (defaults to https://gitlab.com)
GITLAB_PROJECT_ID No Default project ID or path (e.g., my-group/my-project)
LOG_LEVEL No Minimum log level: debug, info, warning, error (default: info)

* One of GITLAB_PAT or GITLAB_OAUTH_TOKEN is required.

Important: When the server is launched as an MCP subprocess (by Claude Code, OpenCode, etc.), the .env file is not loaded automatically. Configure environment variables through your MCP client's config (see the Claude Code and OpenCode examples above). The .env file is only used when running the server directly via bun run start or bun run dev.

Creating Tokens

Personal Access Token (PAT) — Recommended

  1. Go to your GitLab instance → click your Avatar (top-right) → Edit profile
  2. In the left sidebar, select AccessPersonal access tokens
  3. Select Add new token
  4. Fill in:
    • Token name: e.g., mcp-code-review
    • Expiration date: set an appropriate date (max 365 days)
    • Scopes: select api (required for full read/write access to MRs, discussions, labels, approvals)
  5. Select Create personal access token
  6. Copy the token immediately — it won't be shown again
  7. Set it as GITLAB_PAT in your environment or MCP client config

The token will look like glpat-xxxxxxxxxxxxxxxxxxxx.

For full details, see the GitLab PAT documentation.

OAuth 2.0 Token (Alternative)

If you have an OAuth 2.0 access token obtained through an OAuth2 authorization flow, set it as GITLAB_OAUTH_TOKEN. The server sends it via the Authorization: Bearer header.

Note: OAuth tokens expire after 2 hours and require external refresh. For MCP server use, PATs are recommended as they have longer lifetimes and don't require a refresh flow.

Default Project ID

Most tools require a project_id parameter. You can skip passing it explicitly by configuring a default:

  1. Environment variable: Set GITLAB_PROJECT_ID in your .env
  2. Runtime setting: Call set_setting(key: "default_project_id", value: "group/project")
  3. Explicit parameter: Always wins when provided

Priority: explicit param > runtime setting > env var.

Available Tools

Merge Request Tools (13)

Tool Description
get_merge_request Fetch enriched MR details (approvals, commits, deployments)
get_mr_diff Get MR diff with file filtering and generated-file exclusion
get_mr_discussions List all discussion threads on an MR
get_mr_commits List commits in an MR
get_mr_file_content Read a file from the MR branch (base64 decoded)
get_mr_pipelines Get CI/CD pipeline status for an MR
get_mr_changes_since Diff changes since a previous review SHA
list_merge_requests List MRs with filtering (state, labels, scope, search)
create_merge_request_note Post a comment on an MR
create_mr_discussion_reply Reply to an existing discussion thread
resolve_merge_request_thread Resolve or unresolve a discussion thread
approve_merge_request Approve an MR (with optional SHA safety check)
unapprove_merge_request Remove approval from an MR

Label Tools (4)

Tool Description
get_project_labels List all labels in a project
add_mr_label Add a label to an MR
remove_mr_label Remove a label from an MR
set_mr_labels Replace all labels on an MR

Review Session Tools (4)

Tool Description
start_review Start or resume a review session (detects re-reviews, tracks SHA)
add_review_comment Add a comment or suggestion to the review (posts to GitLab inline)
get_review_status Get review progress with item details and resolution status
complete_review Finalize review: update status, post summary, set labels, approve

Settings Tools (2)

Tool Description
get_setting Read a configuration value
set_setting Store a configuration value

Usage with AI Code Reviewer

The typical review workflow:

1. start_review        -- Start a session, detect re-reviews
2. get_merge_request   -- Fetch MR details, approvals, CI status
3. get_mr_diff         -- Read the diff (auto-filters generated files)
4. get_mr_file_content -- Read full files for context
5. add_review_comment  -- Post inline comments and suggestions
6. complete_review     -- Post summary, set labels, approve/request changes

On re-review, start_review automatically detects previous sessions and provides:

  • Resolution status of prior comments
  • Whether new commits have been pushed since last review
  • A diff of changes since the last review via get_mr_changes_since

Architecture

MCP Client (AI Agent)
       |
       | stdio (JSON-RPC)
       |
  MCP Server (Bun)
   /        \
GitLab API   SQLite
 (REST)     (bun:sqlite)
  • Transport: stdio (stdin/stdout for JSON-RPC, stderr for logs)
  • GitLab Client: HTTP with retry/backoff, pagination, structured errors
  • Database: SQLite for review sessions, review items, and settings
  • Logging: Dual-mode -- JSON to stderr + MCP logging messages to client

Development

bun run dev          # Start with hot reload
bun run start        # Start server (stdio transport)
bun run typecheck    # TypeScript type checking
bun test             # Run all tests
bun run lint         # Lint with Biome
bun run format       # Format with Biome
bun run check        # Lint + format with Biome
bun run build        # Build to dist/

Project Structure

src/
  index.ts              Entry point
  server.ts             MCP server setup, environment validation
  logger.ts             Structured logging (stderr JSON + MCP)
  tools/
    index.ts            Tool registry with auto-logging wrapper
    merge-requests.ts   13 MR tools
    labels.ts           4 label tools
    reviews.ts          4 review session tools
    settings.ts         2 settings tools
  gitlab/
    client.ts           REST client (22 methods, retry, pagination)
    auth.ts             PAT/OAuth token handling
    errors.ts           GitLabApiError with classification
    types.ts            GitLab API response types
  db/
    index.ts            SQLite init, migrations, singleton
    schema.ts           Table DDL + TypeScript interfaces
    queries.ts          Typed query helpers
  schemas/
    index.ts            Zod schemas for all tool inputs
tests/
  16 test files, 351 tests, 698 expect() calls

Adding a New Tool

  1. Add Zod schema to src/schemas/index.ts
  2. Add tool registration in the appropriate src/tools/*.ts file
  3. If new file, export register function and add to src/tools/index.ts
  4. Add tests
  5. Run bun run typecheck && bun test && bun run check

License

ISC

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured