MCP Server Demo
Provides Docker image building and registry management capabilities, with templates for building and pushing Docker images to registries.
Offers comprehensive GitLab CI template management with specialized templates for build configurations, security scanning (SAST/SCA), and reusable components for CI/CD pipeline generation.
Includes build tools and CI templates specifically for Python applications, enabling automated building and deployment of Python projects.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Server Demogenerate a GitLab CI template for a Java project with SAST scanning"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server Demo
A demonstration Model Context Protocol (MCP) server that provides tools and resources for Java CI/CD workflows and GitLab CI template management.
π Repository Structure
mcp-server-demo/
βββ main.py # Main entry point for the MCP server
βββ server.py # MCP server implementation with tools and resources
βββ pyproject.toml # Python project configuration and dependencies
βββ uv.lock # Dependency lock file
βββ .gitlab-ci.yml # GitLab CI configuration (template-based)
βββ ci-templates/ # GitLab CI template resources
β βββ build.gitlab-ci.yml
β βββ sast-scanner.gitlab-ci.yml
β βββ sca-scanner.gitlab-ci.yml
β βββ build-image.gitlab-ci.yml
βββ README.md # This fileRelated MCP server: Multi-service MCP Server
π Features
MCP Server Tools
Mathematical Operations:
sum_two_numbers()- Add two integersCI/CD Template Management: Access to GitLab CI template resources
Java Build Tools: Maven-based build commands and configurations
Security Scanning: SAST and SCA scanning capabilities
Docker Operations: Image building and registry management
Deployment Automation: Environment-specific deployment commands
GitLab CI Templates
Build Templates: Java and Python build configurations
Security Scanning: SAST and SCA scanning templates
Docker Integration: Image build and push templates
Reusable Components: Template-based CI/CD pipeline generation
π οΈ Setup Instructions
Prerequisites
Python 3.13 or higher
uv package manager
Installation
Clone the repository
git clone <repository-url> cd mcp-server-demoInstall dependencies
uv syncVerify installation
uv run python --version
π Starting the MCP Server
Method 1: Using uv run
uv run mcp dev main.pyMethod 2: Direct execution
uv run python main.pyMethod 3: Using the MCP CLI
uv run mcp run main.pyπ§ Configuration
Environment Variables
The server uses standard MCP configuration. No additional environment variables are required for basic operation.
GitLab CI Templates
The ci-templates/ directory contains reusable GitLab CI templates:
build.gitlab-ci.yml- Build job templates for Java and Pythonsast-scanner.gitlab-ci.yml- Static Application Security Testing templatessca-scanner.gitlab-ci.yml- Software Composition Analysis templatesbuild-image.gitlab-ci.yml- Docker image build and push templates
π Usage Examples
Using the MCP Server Tools
Add two numbers
result = sum_two_numbers(5, 3) # Returns 8Access CI templates
# Access build template build_template = read_build_ci_file() # Access SAST scanner template sast_template = read_sast_scanner_ci_file()
Generating GitLab CI Files
The server provides resources to help generate GitLab CI configurations using the available templates:
Java Application CI
Uses
.build-javatemplateIncludes SAST and SCA scanning
Docker image building and deployment
Python Application CI
Uses
.build-pythontemplateIncludes security scanning
Container deployment capabilities
π Available Resources
File Resources
file://ci-templates/build.gitlab-ci.yml- Java and Python build templatesfile://ci-templates/sast-scanner.gitlab-ci.yml- SAST scanning templatesfile://ci-templates/sca-scanner.gitlab-ci.yml- SCA scanning templatesfile://ci-templates/build-image.gitlab-ci.yml- Docker build templates
Dynamic Resources
greeting://{name}- Personalized greeting messagespipeline://{project_name}- CI/CD pipeline statusproject://{project_name}- Project informationtemplate://{template_name}- Template information
π§ͺ Testing
Run the server in development mode
uv run mcp dev main.pyTest individual tools
uv run python -c "from server import sum_two_numbers; print(sum_two_numbers(10, 5))"π¦ Dependencies
Core Dependencies
mcp[cli]>=1.11.0- Model Context Protocol server and CLI tools
Development Dependencies
Python 3.13+ for modern language features
uv for fast dependency management
π€ Contributing
Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request
π License
This project is licensed under the MIT License - see the LICENSE file for details.
π Troubleshooting
Common Issues
MCP server not starting
Ensure Python 3.13+ is installed
Verify dependencies with
uv syncCheck for port conflicts
Template access issues
Verify
ci-templates/directory existsCheck file permissions
Ensure template files are properly formatted
GitLab CI generation problems
Verify template syntax
Check for missing template dependencies
Ensure proper job inheritance
Getting Help
Check the MCP documentation
Review the template files in
ci-templates/Examine the server implementation in
server.py
π Related Links
Available Tools
1 toolsum_two_numbersC
Add two numbers
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Add two numbers' only states the operation without any information about side effects, error handling, performance, or return format. It fails to provide meaningful behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just three words, front-loading the core purpose without any waste. Every word earns its place, making it highly efficient for an AI agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (simple arithmetic) and the presence of an output schema (which handles return values), the description is somewhat adequate. However, with no annotations and 0% schema coverage, it lacks details on behavior and parameters that could be helpful for more robust use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no descriptions for parameters 'a' and 'b'. The description 'Add two numbers' implies these are numbers to be added but doesn't specify their roles (e.g., which is first operand), constraints, or examples. It adds minimal semantic value beyond the schema's type definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Add two numbers' clearly states the tool's purpose with a specific verb ('Add') and resource ('two numbers'). It's unambiguous about what the tool does. However, with no sibling tools to differentiate from, it cannot achieve the full 5 points for sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions. With no sibling tools, there's no explicit comparison, but it lacks even basic usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools, making it trivially distinct.
The single tool name 'sum_two_numbers' follows a clear verb_noun pattern, and with only one tool, consistency is inherently perfect.
A single tool is generally too few for a server's purpose unless it's extremely narrow; this feels thin and may not cover a meaningful domain adequately.
The tool surface is severely incomplete, as a single addition tool does not provide any meaningful coverage or lifecycle for a domain, leaving obvious gaps for any practical use.
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