Quack MCP Server

# Quack MCP Server Quack is a continuous integration server built as an MCP server that automates code analysis and testing for Python code. It provides tools for linting and static type analysis of Python code. ## Features - **Linting**: Analyzes Python code for style, formatting, and code quality issues using pylint. - **Static Analysis**: Performs static type checking using mypy to identify type errors. - **Asynchronous Processing**: Jobs are processed asynchronously, allowing for concurrent analysis of multiple code submissions. - **Job Management**: Track and retrieve results of submitted jobs. ## Installation 1. Clone the repository: ```bash git clone https://github.com/yourusername/quack.git cd quack ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` The Quack server requires the following dependencies: ``` mcp[cli] pylint mypy pytest pytest-asyncio ``` ## Usage ### Starting the Server To start the Quack server with stdio transport (default): ```bash python3 quack.py ``` For debug logging: ```bash python3 quack.py --debug ``` To start the server with SSE (Server-Sent Events) transport for HTTP communication: ```bash python3 quack.py --sse --host=0.0.0.0 --port=8000 ``` Alternatively, you can use the provided shell script: ```bash # For stdio transport (default) ./run_quack.sh # For SSE transport ./run_quack.sh --sse --host=0.0.0.0 --port=8000 ``` ### Docker Container The Quack server can be run in a Docker container, which automatically uses SSE transport: ```bash # Build the Docker image docker build -t quack-mcp-server . # Run the container, exposing port 8000 docker run -p 8000:8000 quack-mcp-server ``` 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 ``` tests/ ├── server/ # Tests OF the server functionality │ ├── test_server_direct.py # Direct testing of job manager │ ├── test_server_auto.py # Auto-starts and stops the server │ └── test_server_client.py # Tests the MCP client interface ├── processors/ # Tests OF the processors │ ├── test_lint_processor.py # Tests for lint processor │ └── test_static_analysis_processor.py # Tests for static analysis └── examples/ # Example submissions for testing BY the server └── example_code.py # Contains intentional issues for testing ``` 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: ```bash python -m pytest tests/ --asyncio-mode=auto ``` 2. Run a specific test file: ```bash python -m pytest tests/server/test_server_direct.py -v --asyncio-mode=auto ``` 3. Run tests for a specific processor: ```bash python -m pytest tests/processors/test_lint_processor.py -v ``` 4. Run tests with verbose output and show test progress: ```bash python -m pytest tests/ -v --asyncio-mode=auto ``` 5. Run tests and stop on the first failure: ```bash python -m pytest tests/ -x --asyncio-mode=auto ``` #### 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) ```json { "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: ```json { "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: ```python # Linting issues unused_var = 42 # Unused variable x = 10 # Single-letter variable name # Type issues def add(a: int, b: int) -> int: return a + b # Function with both linting and type issues def calculate_average(numbers): # Missing type annotations total = 0 for num in numbers: total += num unused_result = total * 2 # Unused variable return total / len(numbers) # Call with wrong type result = add("5", 10) # Type error: str + int ``` ### 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: ```bash 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: ```bash docker logs <container_id> ``` 4. **Testing the Connection**: You can test if the SSE endpoint is accessible: ```bash 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: ```bash python3 quack.py --debug ``` Logs are written to both the console and `logs/quack.log`.