gitlab-pipeline-analyzer 0.1.2

FastMCP server for analyzing GitLab CI/CD pipeline failures

GitLab Pipeline Analyzer MCP Server

A FastMCP server that analyzes GitLab CI/CD pipeline failures, extracts errors and warnings from job traces, and returns structured JSON responses.

Features

• Analyze failed GitLab CI/CD pipelines by pipeline ID
• Extract failed jobs from pipelines
• Retrieve and parse job traces
• Extract errors and warnings from logs
• Return structured JSON responses for AI analysis
• Support for Python projects with lint, test, and build stages
• Multiple transport protocols: STDIO, HTTP, and SSE

Installation

# Install dependencies
uv pip install -e .

# Or with pip
pip install -e .

Configuration

Set the following environment variables:

export GITLAB_URL="https://gitlab.com"  # Your GitLab instance URL
export GITLAB_TOKEN="your-access-token"  # Your GitLab personal access token

# Optional: Configure transport settings
export MCP_HOST="127.0.0.1"  # Host for HTTP/SSE transport (default: 127.0.0.1)
export MCP_PORT="8000"       # Port for HTTP/SSE transport (default: 8000)
export MCP_PATH="/mcp"       # Path for HTTP transport (default: /mcp)

Note: Project ID is now passed as a parameter to each tool, making the server more flexible.

Running the Server

The server supports three transport protocols:

1. STDIO Transport (Default)

Best for local tools and command-line scripts:

# Default STDIO transport
python server.py

# Or explicitly specify
python server.py --transport stdio

2. HTTP Transport

Recommended for web deployments and remote access:

# Start HTTP server
python http_server.py

# Or using the main server script
python server.py --transport http --host 127.0.0.1 --port 8000 --path /mcp

# Or using environment variables
MCP_TRANSPORT=http MCP_HOST=0.0.0.0 MCP_PORT=8080 python server.py

The HTTP server will be available at: http://127.0.0.1:8000/mcp

3. SSE Transport

For compatibility with existing SSE clients:

# Start SSE server
python sse_server.py

# Or using the main server script
python server.py --transport sse --host 127.0.0.1 --port 8000

The SSE server will be available at: http://127.0.0.1:8000

Using with MCP Clients

HTTP Transport Client Example

from fastmcp.client import Client

# Connect to HTTP MCP server
async with Client("http://127.0.0.1:8000/mcp") as client:
    # List available tools
    tools = await client.list_tools()

    # Analyze a pipeline
    result = await client.call_tool("analyze_pipeline", {
        "project_id": "123",
        "pipeline_id": "456"
    })

Configuration for Multiple Servers

config = {
    "mcpServers": {
        "gitlab": {
            "url": "http://127.0.0.1:8000/mcp",
            "transport": "http"
        }
    }
}

async with Client(config) as client:
    result = await client.call_tool("gitlab_analyze_pipeline", {
        "project_id": "123",
        "pipeline_id": "456"
    })

Development

Setup

# Install dependencies
uv sync --all-extras

# Install pre-commit hooks
uv run pre-commit install

Running tests

# Run all tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=gitlab_analyzer --cov-report=html

# Run security scans
uv run bandit -r src/

Code quality

# Format code
uv run ruff format

# Lint code
uv run ruff check --fix

# Type checking
uv run mypy src/

GitHub Actions

This project includes comprehensive CI/CD workflows:

CI Workflow (.github/workflows/ci.yml)

• Triggers: Push to main/develop, Pull requests
• Features:
  • Tests across Python 3.10, 3.11, 3.12
  • Code formatting with Ruff
  • Linting with Ruff
  • Type checking with MyPy
  • Security scanning with Bandit
  • Test coverage reporting
  • Build validation

Release Workflow (.github/workflows/release.yml)

• Triggers: GitHub releases, Manual dispatch
• Features:
  • Automated PyPI publishing with trusted publishing
  • Support for TestPyPI deployment
  • Build artifacts validation
  • Secure publishing without API tokens

Security Workflow (.github/workflows/security.yml)

• Triggers: Push, Pull requests, Weekly schedule
• Features:
  • Bandit security scanning
  • Trivy vulnerability scanning
  • SARIF upload to GitHub Security tab
  • Automated dependency scanning

Setting up PyPI Publishing

1. Configure PyPI Trusted Publishing:

   • Go to PyPI or TestPyPI
   • Add a new trusted publisher with:
     • PyPI project name: gitlab-pipeline-analyzer
     • Owner: your-github-username
     • Repository name: your-repo-name
     • Workflow name: release.yml
     • Environment name: pypi (or testpypi)

2. Create GitHub Environment:

   • Go to repository Settings → Environments
   • Create environments named pypi and testpypi
   • Configure protection rules as needed

3. Publishing:

   • TestPyPI: Use workflow dispatch in Actions tab
   • PyPI: Create a GitHub release to trigger automatic publishing

Pre-commit Hooks

The project uses pre-commit hooks for code quality:

# Install hooks
uv run pre-commit install

# Run hooks manually
uv run pre-commit run --all-files

Hooks include:

• Trailing whitespace removal
• End-of-file fixing
• YAML/TOML validation
• Ruff formatting and linting
• MyPy type checking
• Bandit security scanning

Usage

Running the server

# Run with Python
python gitlab_analyzer.py

# Or with FastMCP CLI
fastmcp run gitlab_analyzer.py:mcp

Available tools

1. analyze_failed_pipeline(project_id, pipeline_id) - Analyze a failed pipeline by ID
2. get_pipeline_jobs(project_id, pipeline_id) - Get all jobs for a pipeline
3. get_job_trace(project_id, job_id) - Get trace log for a specific job
4. extract_log_errors(log_text) - Extract errors and warnings from log text
5. get_pipeline_status(project_id, pipeline_id) - Get basic pipeline status

Example

import asyncio
from fastmcp import Client

async def analyze_pipeline():
    client = Client("gitlab_analyzer.py")
    async with client:
        result = await client.call_tool("analyze_failed_pipeline", {
            "project_id": "19133",  # Your GitLab project ID
            "pipeline_id": 12345
        })
        print(result)

asyncio.run(analyze_pipeline())

Environment Setup

Create a .env file with your GitLab configuration:

GITLAB_URL=https://gitlab.com
GITLAB_TOKEN=your-personal-access-token

Development

# Install development dependencies
uv sync

# Run tests
uv run pytest

# Run linting and type checking
uv run tox -e lint,type

# Run all quality checks
uv run tox

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Siarhei Skuratovich

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run the test suite
5. Submit a pull request

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Note: This MCP server is designed to work with GitLab CI/CD pipelines and requires appropriate API access tokens.