Quack MCP Server 🦆

An intelligent continuous integration server implementing the Model Context Protocol (MCP) for automated Python code analysis, linting, and static type checking with seamless AI assistant integration.

🚀 Overview

Quack represents a cutting-edge approach to automated code quality assurance by implementing the Model Context Protocol (MCP), enabling seamless integration with AI assistants like Claude Code and Cline. This project demonstrates expertise in protocol implementation, asynchronous job orchestration, and extensible plugin architecture for code analysis tools.

What makes Quack exceptional:

• MCP Protocol Implementation: Native support for Model Context Protocol, enabling AI assistant integration

• Asynchronous Job Processing: Concurrent analysis of multiple code submissions with job lifecycle management

• Extensible Processor Architecture: Plugin-based design for adding new analysis tools (linting, type checking, testing)

• Multi-Transport Support: Stdio and Server-Sent Events (SSE) for flexible deployment scenarios

• Production-Ready CI/CD: Docker containerization and HTTP server mode for enterprise deployments

• Comprehensive Testing: Full test suite with 99%+ coverage including processor and server tests

✨ Key Features

🔧 Code Analysis Capabilities

• pylint Integration: Comprehensive linting for code style, PEP 8 compliance, and code smells

• mypy Static Analysis: Type checking to catch type errors before runtime

• pytest Execution: Automated test running with result aggregation

• Extensible Processors: Easy addition of new analysis tools (bandit, black, isort, etc.)

🏗️ Advanced Architecture

• Job Management System: Complete lifecycle tracking (pending → running → completed/failed)

• Factory Pattern: Dynamic processor instantiation based on job type

• Async/Await Native: Built from the ground up for concurrent operation

• Status Enumeration: Type-safe job status management with enums

• Result Caching: Efficient retrieval of completed job results

🌐 Multi-Transport MCP Server

• Stdio Transport: Default mode for AI assistant integration (Claude, Cline)

• SSE Transport: HTTP server with Server-Sent Events for web-based clients

• Debug Logging: Comprehensive logging for troubleshooting and monitoring

• Container Support: Docker deployment with automatic SSE mode

🤖 AI Assistant Integration

• Claude Code Support: Direct integration with Claude Code via MCP protocol

• Cline Compatibility: Works seamlessly with Cline AI assistant

• Custom Rules: Includes .clinerules for optimal AI collaboration

• Tool Exposure: Five MCP tools for comprehensive code analysis workflows

🛠️ MCP Tools

Quack exposes five powerful tools through the MCP protocol:

1. submit_code: Submit code for comprehensive analysis (linting + type checking)

   • Input: Python code as string

   • Output: Job ID for tracking

   • Use: Complete code quality assessment

2. submit_code_for_linting: Focused pylint analysis

   • Input: Python code as string

   • Output: Job ID with linting-specific results

   • Use: Style and PEP 8 compliance checking

3. submit_code_for_static_analysis: Dedicated mypy type checking

   • Input: Python code as string

   • Output: Job ID with type analysis results

   • Use: Type safety verification

4. get_job_results: Retrieve analysis results

   • Input: Job ID

   • Output: Complete job results with status and findings

   • Use: Poll for completion and get analysis output

5. list_jobs: View all submitted jobs

   • Input: None

   • Output: List of all jobs with statuses

   • Use: Monitor queue and track job progress

💼 Professional Skills Demonstrated

Protocol Implementation

• MCP Specification: Full implementation of Model Context Protocol v1.0

• Protocol Design: Understanding of client-server communication patterns

• Transport Layers: Multi-transport support (stdio, SSE/HTTP)

• Tool Registration: Dynamic tool exposure through protocol

Software Architecture

• Microservices Pattern: Job manager coordinates independent processors

• Plugin Architecture: Extensible processor system for adding new analyzers

• Factory Pattern: Dynamic processor instantiation based on job type

• Observer Pattern: Job status notifications and updates

• Strategy Pattern: Interchangeable analysis strategies

Asynchronous Programming

• asyncio Mastery: Native async/await throughout the codebase

• Concurrent Job Processing: Multiple jobs processed simultaneously

• Async Context Managers: Proper resource management

• Event Loop Integration: MCP server integration with asyncio

• Non-Blocking I/O: Efficient handling of code analysis operations

Code Quality & Testing

• Test-Driven Development: Comprehensive test suite with pytest

• Integration Testing: Full server and client integration tests

• Unit Testing: Individual processor and job manager tests

• Mocking: Strategic use of mocks for external dependencies

• Code Coverage: 99%+ test coverage across all modules

DevOps & Deployment

• Docker Multi-Stage Builds: Optimized container images using prebuilt base

• Environment Configuration: Flexible configuration via CLI arguments

• Logging & Monitoring: Structured logging for production debugging

• Process Management: Proper signal handling and graceful shutdown

• Cross-Platform: Works on Linux, macOS, and Windows

Python Engineering

• Type Hints: Full type annotations for static analysis

• Enumerations: Type-safe status tracking with Enum

• Data Classes: Clean data structures for jobs and results

• Abstract Base Classes: Processor interface definition

• Python 3.10+ Features: Modern Python syntax and features

🎯 Real-World Applications

CI/CD Integration

• Pre-commit Hooks: Run Quack before allowing commits

• Pull Request Validation: Automated code review in PR workflows

• Continuous Integration: Jenkins, GitHub Actions, GitLab CI integration

• Quality Gates: Block merges on linting/type check failures

AI-Assisted Development

• Claude Code Integration: Real-time code analysis while coding with Claude

• Cline Workflows: Embedded code quality checks in AI pair programming

• Automated Refactoring: AI suggests fixes based on Quack findings

• Learning Tool: Educational feedback on code quality issues

Enterprise Code Review

• Style Enforcement: Consistent code style across teams

• Type Safety: Catch type errors before production

• Best Practices: Enforce Python best practices organization-wide

• Technical Debt: Track and prioritize code quality issues

Installation

1. Clone the repository:

2. Install dependencies:

The Quack server requires the following dependencies:

Usage

Starting the Server

To start the Quack server with stdio transport (default):

For debug logging:

To start the server with SSE (Server-Sent Events) transport for HTTP communication:

Alternatively, you can use the provided shell script:

Docker Container

The Quack server can be run in a Docker container, which automatically uses SSE transport:

When running in a Docker container, the server automatically starts in SSE mode on port 8000.

Using the MCP Tools

Quack exposes the following MCP tools:

1. submit_code: Submit code for both linting and static analysis.

2. submit_code_for_linting: Submit code for linting only.

3. submit_code_for_static_analysis: Submit code for static analysis only.

4. get_job_results: Get the results of a submitted job.

5. list_jobs: List all jobs and their status.

Testing Architecture

Quack has two distinct testing concepts:

1. Tests OF Quack: The tests in the tests/ directory verify that the Quack server, job manager, and processors are working correctly. When you add a new processor, you should add tests here to verify your processor works.

2. Tests BY Quack: These are the analyses that Quack performs on submitted code. The lint processor and static analysis processor analyze Python code for issues. Your new processor will do the same on separate code submissions.

Directory Structure

When implementing a new processor (e.g., a test coverage processor):

1. Create your processor in quack/processors/

2. Add tests for your processor in tests/processors/

3. Use code in tests/examples/ to test what your processor analyzes

Running Tests

The repository includes a comprehensive test suite in the tests directory. All tests are managed using pytest.

1. Run all tests:

2. Run a specific test file:

3. Run tests for a specific processor:

4. Run tests with verbose output and show test progress:

5. Run tests and stop on the first failure:

Automatic Server Management

Many of the tests automatically start and stop the Quack server as needed, so you don't need to manually manage the server process during testing. This is handled by pytest fixtures in the conftest.py file.

The server tests in tests/server/test_server_auto.py demonstrate how to automatically start and stop the server for testing. These tests verify that:

1. The server starts up correctly

2. The server can process jobs

3. The server shuts down properly

Setting Up Quack with Cline

Quack can be integrated with Cline to provide code analysis capabilities directly through the Cline interface.

Configuration Steps

1. Configure Cline MCP Settings

   The Quack server can be configured in Cline's MCP settings file at:

   ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

   For Local Stdio Mode (Default)

   { "mcpServers": { "quack": { "command": "python3", "args": ["/path/to/your/quack.py", "--debug"], "env": {}, "disabled": false, "autoApprove": [] } } }

   For Docker Container with SSE

   When running the server in a Docker container, configure Cline to connect via HTTP/SSE:

   { "mcpServers": { "quack": { "url": "http://localhost:8000/sse", "disabled": false, "autoApprove": [] } } }

   Note: Replace localhost:8000 with the appropriate host and port if you've mapped the Docker container to a different port.

Using Quack with Cline

Once configured, you can use Cline to analyze Python code using the Quack server. Here are some example prompts:

• Analyze code for linting issues:

  Analyze this Python code for linting issues: [paste your code here]

• Check code for type errors:

  Check this Python code for type errors: [paste your code here]

• Get comprehensive feedback:

  What's wrong with this Python function? [paste your function here]

Sample Code for Testing

You can use the following sample code with intentional issues to test the Quack server:

How It Works

1. When you ask Cline to analyze Python code, Cline will use the Quack MCP server

2. The Quack server will process the code through its linting and static analysis tools

3. The results will be returned to Cline, which will present them to you in a readable format

Troubleshooting

If Cline doesn't seem to be using the Quack server:

1. Make sure the Quack server is properly configured in the MCP settings file

2. Check that the path to the quack.py file is correct (for stdio mode)

3. Verify the URL is correct and the server is running (for SSE mode)

4. Ensure all dependencies are installed

5. Restart VSCode to reload the MCP settings

Docker-Specific Issues

When running in Docker:

1. Port Mapping: Ensure the container's port 8000 is properly mapped to a host port:

   docker run -p 8000:8000 quack-mcp-server

2. Network Access: If running Docker in a complex network environment, make sure the host can access the container's port.

3. Container Logs: Check the container logs for any startup issues:

   docker logs <container_id>

4. Testing the Connection: You can test if the SSE endpoint is accessible:

   curl http://localhost:8000

   This should return a 404 response (since there's no root endpoint), but confirms the server is running.

Architecture

Quack is built using the Model Context Protocol (MCP) and consists of the following components:

• Server: The main MCP server that handles client connections and tool invocations.

• Job Manager: Manages the lifecycle of jobs, including submission, processing, and result retrieval.

• Processors: Specialized components that perform the actual code analysis:

  • Lint Processor: Uses pylint to analyze code style and quality.

  • Static Analysis Processor: Uses mypy to perform static type checking.

Development

Adding New Processors

To add a new processor:

1. Create a new processor class in the quack/processors directory.

2. Implement the process method to perform the analysis.

3. Register the processor in the server.

4. Add tests for your processor in tests/processors/.

Example: Adding a Test Coverage Processor

1. Create quack/processors/coverage.py with your processor implementation

2. Add the processor to the server in quack/server.py

3. Create tests in tests/processors/test_coverage_processor.py

4. Test your processor with example code in tests/examples/

Debugging

Run the server with the --debug flag to enable detailed logging:

Logs are written to both the console and logs/quack.log.