mcp_testlab copilot-instructions.md

Copilot Instructions

These instructions help GitHub Copilot understand how to assist with the MCP Testlab project.

Project Overview

This project demonstrates using the Model Context Protocol (MCP). It uses the FastMCP Python SDK to create tools that can be accessed via multiple transport protocols. The main focus is on a sentiment analysis tool that can analyze text and return sentiment scores.

• An MCP server FastMCP Server
• An MCP client FastMCP Client
• Multiple MCP clients using different transport protocols (stdio, SSE, streamable-http)
• Command-line interfaces to interact with the sentiment analysis tool

Coding Style

When contributing to this project, follow these coding style guidelines:

1. Python Style:

   • Follow PEP 8 conventions for Python code
   • Allow line length up to 120 characters
   • Use PascalCase for class names (e.g., SentimentAnalyzer)
   • Use snake_case for variables and functions (e.g., analyze_text, sentiment_score)

2. Type Hints: Include type annotations for function parameters and return values

3. Documentation:

   • Add docstrings to all functions and classes
   • Use Google-style docstrings with Args and Returns sections

4. Error Handling:

   • Use try/except blocks for error handling
   • Provide descriptive error messages

5. Async Programming:

   • Use asyncio for asynchronous operations
   • Employ async context managers with AsyncExitStack for resource cleanup

6. Command-line Interfaces:

   • Use argparse for processing command-line arguments
   • Provide helpful descriptions for all arguments

7. Code Organization:

   • Keep classes focused on a single responsibility
   • Use clear, descriptive variable and function names
   • Group related functionality within classes

8. LLM Prompt

   • when a prompt will span muliple lines use the triple quotes """ to enclose the prompt text.
   • use textwrap.dedent to remove any common leading whitespace.