devops-mcps 0.7.1

A FastMCP-based MCP server for DevOps

DevOps MCP Server

A FastMCP-based MCP server providing a suite of DevOps tools and integrations.

This server operates in a read-only manner, retrieving data for analysis and display without modifying your systems. It's designed with safety in mind for DevOps environments.

Certified by MCP Review

Features

The DevOps MCP Server integrates with various essential DevOps platforms:

GitHub Integration

• Repository Management: Search and view repository details.
• File Access: Retrieve file contents from repositories.
• Issue Tracking: Manage and track issues.
• Code Search: Perform targeted code searches.
• Commit History: View commit history for branches.
• Public & Enterprise Support: Automatically detects and connects to both public GitHub and GitHub Enterprise instances (configurable via GITHUB_API_URL).

Jenkins Integration

• Job Management: List and manage Jenkins jobs.
• Build Logs: Retrieve and analyze build logs.
• View Management: Access and manage Jenkins views.
• Build Parameters: Inspect parameters used for builds.
• Failure Monitoring: Identify and monitor recent failed builds.

Artifactory Integration

• Repository Browsing: List items (files and directories) within Artifactory repositories.
• Artifact Search: Search for artifacts by name or path across multiple repositories using Artifactory Query Language (AQL).
• Item Details: Retrieve metadata and properties for specific files and directories.
• Authentication: Supports both token-based and username/password authentication.

Installation

Install the package using pip:

pip install devops-mcps

Usage

Run the MCP server directly:

devops-mcps

Transport Configuration

The server supports two communication transport types:

• stdio (default): Standard input/output.
• stream_http: HTTP streaming transport.

Local Usage:

# Default stdio transport
devops-mcps

# stream_http transport (runs HTTP server on 127.0.0.1:3721/mcp by default)
devops-mcps --transport stream_http

UVX Usage:

If using UVX, first install the tools:

uvx install

Then run:

# Default stdio transport
uvx run devops-mcps

# stream_http transport
uvx run devops-mcps-stream-http

Configuration

Configure the server using environment variables:

Required:

# GitHub
export GITHUB_PERSONAL_ACCESS_TOKEN="your_github_token"
# Optional: For GitHub Enterprise, set your API endpoint
# export GITHUB_API_URL="https://github.mycompany.com"

# Jenkins
export JENKINS_URL="your_jenkins_url"
export JENKINS_USER="your_jenkins_username"
export JENKINS_TOKEN="your_jenkins_api_token_or_password"

# Artifactory
export ARTIFACTORY_URL="https://your-artifactory-instance.example.com"
# Choose ONE authentication method:
export ARTIFACTORY_IDENTITY_TOKEN="your_artifactory_identity_token"
# OR
export ARTIFACTORY_USERNAME="your_artifactory_username"
export ARTIFACTORY_PASSWORD="your_artifactory_password"

Optional:

# Jenkins Log Length (default: 5120 bytes)
export LOG_LENGTH=10240

# MCP Server Port for stream_http transport (default: 3721)
export MCP_PORT=3721

# Dynamic Prompts (optional)
export PROMPTS_FILE="example_prompts.json"

Note: LOG_LENGTH controls the amount of Jenkins log data retrieved. Adjust as needed.

Alternative: Using .env file

You can also create a .env file in the project root directory instead of setting environment variables manually:

# .env file
GITHUB_PERSONAL_ACCESS_TOKEN=your_github_token_here
PROMPTS_FILE=example_prompts.json
# Add other optional environment variables as needed

The server will automatically load environment variables from the .env file when it starts.

Dynamic Prompts

The server supports loading custom prompts from a JSON file. Set the PROMPTS_FILE environment variable to the path of your prompts configuration file.

Prompts File Format:

{
  "prompts": [
    {
      "name": "github_repo_analysis",
      "description": "Analyze a GitHub repository for DevOps insights",
      "template": "Please analyze the GitHub repository {{owner}}/{{repo}} and provide insights on:\n\n1. Repository structure and organization\n2. CI/CD pipeline configuration\n3. Code quality indicators\n4. Security considerations\n5. Documentation quality\n\n{{#include_issues}}Also include analysis of recent issues and their resolution patterns.{{/include_issues}}",
      "arguments": [
        {
          "name": "owner",
          "description": "GitHub repository owner",
          "required": true
        },
        {
          "name": "repo",
          "description": "GitHub repository name",
          "required": true
        },
        {
          "name": "include_issues",
          "description": "Include analysis of repository issues",
          "required": false
        }
      ]
    }
  ]
}

Using Prompts

The DevOps MCP Server provides dynamic prompts that help you perform common DevOps tasks. Here's how to use the available prompts:

Available Prompts

1. quick_repo_check - Quickly check a GitHub repository's basic information and recent activity
2. build_troubleshoot - Troubleshoot Jenkins build failures with detailed analysis

Using the build_troubleshoot Prompt

Purpose: Analyze Jenkins build failures with detailed analysis and actionable recommendations.

Parameters:

Parameter	Type	Required	Description
job_name	string	✅ Yes	Jenkins job name
build_number	string	❌ No	Build number to analyze (use -1 for latest)
include_logs	boolean	❌ No	Whether to include build logs in analysis

Usage Examples:

# Basic usage (latest build)
Prompt: build_troubleshoot
Parameters:
- job_name: "my-application-build"

# Specific build number
Prompt: build_troubleshoot
Parameters:
- job_name: "my-application-build"
- build_number: "42"

# With build logs
Prompt: build_troubleshoot
Parameters:
- job_name: "my-application-build"
- build_number: "42"
- include_logs: true

What it does:

1. Gets build status and basic information for the specified job
2. Retrieves and analyzes build logs (if include_logs is true)
3. Identifies potential failure causes based on the build data
4. Suggests troubleshooting steps with actionable recommendations

Using the quick_repo_check Prompt

Purpose: Quickly analyze a GitHub repository's basic information and recent activity.

Parameters:

Parameter	Type	Required	Description
repo_name	string	✅ Yes	Repository name in format 'owner/repo'

Usage Example:

Prompt: quick_repo_check
Parameters:
- repo_name: "facebook/react"

What it does:

1. Gets basic repository information
2. Lists recent commits (last 10)
3. Checks open issues count
4. Reviews README content if available
5. Provides a summary of the repository's current state and activity level

Prerequisites

To use Jenkins-related prompts like build_troubleshoot, ensure you have:

# Required Jenkins environment variables
export JENKINS_URL="https://your-jenkins-server.com"
export JENKINS_USER="your-username"
export JENKINS_TOKEN="your-api-token"

To use GitHub-related prompts like quick_repo_check, ensure you have:

# Required GitHub environment variable
export GITHUB_PERSONAL_ACCESS_TOKEN="your_github_token"

Template Variables:

• Use {{variable_name}} for simple variable substitution
• Use {{#variable_name}}...{{/variable_name}} for conditional blocks (shown if variable has a value)
• Use {{^variable_name}}...{{/variable_name}} for negative conditional blocks (shown if variable is empty/null)

Available Tools for Prompts: Your prompts can reference any of the available MCP tools:

• GitHub tools: search_repositories, get_file_contents, list_commits, list_issues, etc.
• Jenkins tools: get_jenkins_jobs, get_jenkins_build_log, get_recent_failed_jenkins_builds, etc.
• Azure tools: get_azure_subscriptions, list_azure_vms, list_aks_clusters, etc.
• Artifactory tools: list_artifactory_items, search_artifactory_items, get_artifactory_item_info, etc.

Docker

Build the Docker image:

docker build -t devops-mcps .

Run the container:

# Stdio transport (interactive)
docker run -i --rm \
  -e GITHUB_PERSONAL_ACCESS_TOKEN="..." \
  -e JENKINS_URL="..." \
  -e JENKINS_USER="..." \
  -e JENKINS_TOKEN="..." \
  -e ARTIFACTORY_URL="..." \
  -e ARTIFACTORY_IDENTITY_TOKEN="..." \
  devops-mcps

# stream_http transport (background, HTTP server on 127.0.0.1:3721/mcp by default)
docker run -d -p 3721:3721 --rm \
  -e TRANSPORT_TYPE=stream_http \
  -e MCP_PORT=3721 \
  -e GITHUB_PERSONAL_ACCESS_TOKEN="..." \
  -e JENKINS_URL="..." \
  -e JENKINS_USER="..." \
  -e JENKINS_TOKEN="..." \
  -e ARTIFACTORY_URL="..." \
  -e ARTIFACTORY_IDENTITY_TOKEN="..." \
  devops-mcps

Replace ... with your actual credentials.

VSCode Integration

Configure the MCP server in VSCode's settings.json:

Example (UVX with stdio):

"devops-mcps": {
  "type": "stdio",
  "command": "uvx",
  "args": ["run", "devops-mcps"],
  "env": {
    "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_...",
    "GITHUB_API_URL": "https://github.mycompany.com", // Optional for GHE
    "JENKINS_URL": "...",
    "JENKINS_USER": "...",
    "JENKINS_TOKEN": "...",
    "ARTIFACTORY_URL": "...",
    "ARTIFACTORY_IDENTITY_TOKEN": "cm..." // Or USERNAME/PASSWORD
  }
}

Example (Docker with stream_http):

Ensure the Docker container is running with stream_http enabled (see Docker section).

{
  "type": "stream_http",
  "url": "http://127.0.0.1:3721/mcp", // Adjust if Docker host is remote or if MCP_PORT is set differently
  "env": {
    // Environment variables are set in the container,
    // but can be overridden here if needed.
    "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_..."
  }
}

Refer to the initial README.md sections for other transport/runner combinations (UVX/stream_http, Docker/stdio).

Development

Set up your development environment:

# Install dependencies (using uv)
uv pip install -e ".[dev]"
# Or sync with lock file
# uv sync --dev

Linting and Formatting (Ruff):

# Check code style
uvx ruff check .

# Format code
uvx ruff format .

Testing (Pytest):

pytest --cov=src/devops_mcps --cov-report=html tests/

Debugging with MCP Inspector:

# Basic run
npx @modelcontextprotocol/inspector uvx run devops-mcps

# Run with specific environment variables
npx @modelcontextprotocol/inspector uvx run devops-mcps -e GITHUB_PERSONAL_ACCESS_TOKEN=... -e JENKINS_URL=... # Add other vars

Checking for package dependencies outdated

uv pip list --outdated

Updating package dependencies

uv lock --upgrade

CI/CD

A GitHub Actions workflow (.github/workflows/ci.yml) handles:

1. Linting & Testing: Runs Ruff and Pytest on pushes and pull requests.
2. Publishing: Builds and publishes the Python package to PyPI and the Docker image to Docker Hub on pushes to the main branch.

Required Repository Secrets:

• PYPI_API_TOKEN: PyPI token for package publishing.
• DOCKER_HUB_USERNAME: Docker Hub username.
• DOCKER_HUB_TOKEN: Docker Hub access token.

Packaging and Publishing (Manual)

Ensure you have build and twine installed:

pip install -U build twine

1. Update Version: Increment the version number in pyproject.toml.
2. Build: python -m build
3. Upload: twine upload dist/* (Requires ~/.pypirc configuration or token input).

Appendix: GitHub Search Query Syntax

Leverage GitHub's powerful search syntax within the MCP tools:

Repository Search (gh_search_repositories):

• in:name,description,readme: Search specific fields. Example: fastapi in:name
• user:USERNAME or org:ORGNAME: Scope search to a user/org. Example: user:tiangolo fastapi
• language:LANGUAGE: Filter by language. Example: http client language:python
• stars:>N, forks:<N, created:YYYY-MM-DD, pushed:>YYYY-MM-DD: Filter by metrics and dates. Example: language:javascript stars:>1000 pushed:>2024-01-01
• topic:TOPIC-NAME: Filter by topic. Example: topic:docker topic:python
• license:LICENSE-KEYWORD: Filter by license (e.g., mit, apache-2.0). Example: language:go license:mit

Code Search (gh_search_code):

• in:file,path: Search file content (default) or path. Example: "import requests" in:file
• repo:OWNER/REPO: Scope search to a specific repository. Example: "JenkinsAPIException" repo:your-org/your-repo
• language:LANGUAGE: Filter by file language. Example: def main language:python
• path:PATH/TO/DIR, filename:FILENAME.EXT, extension:EXT: Filter by path, filename, or extension. Example: "GithubException" path:src/devops_mcps extension:py

References:

• Searching on GitHub
• Searching Code
• Searching Repositories

License

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