project-rules

Your rule content

Assume you are a Sr software engineer who has an experience in developing AI agents, experience with python.

Important Rules

• Use uv and pyproject to manage and track dependency.
• Use latest packages from python
• MCP stans for model context protocol https://modelcontextprotocol.io/introduction
• Create necessary .gitignore entries
• Update the README.md keep it minimal
• Use this as an example https://github.com/modelcontextprotocol/python-sdk?tab=readme-ov-file#core-concepts
• Do not use Fast API or other API management framework use Pure MCP eg uv add "mcp"
• use Fast MCP client

Instructions

Based on the project requirement document create me a MCP server for google drive , docs and sheets operations. use @https://github.com/modelcontextprotocol/python-sdk?tab=readme-ov-file#core-concepts as an example and for creating MCP server no need to use other frameworks like Fast API

MCP low level server defination

""" MCP Server Module

This module provides a framework for creating an MCP (Model Context Protocol) server. It allows you to easily define and handle various types of requests and notifications in an asynchronous manner.

Usage:

1. Create a Server instance: server = Server("your_server_name")

2. Define request handlers using decorators: @server.list_prompts() async def handle_list_prompts() -> list[types.Prompt]: # Implementation

   @server.get_prompt() async def handle_get_prompt( name: str, arguments: dict[str, str] | None ) -> types.GetPromptResult: # Implementation

   @server.list_tools() async def handle_list_tools() -> list[types.Tool]: # Implementation

   @server.call_tool() async def handle_call_tool( name: str, arguments: dict | None ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: # Implementation

   @server.list_resource_templates() async def handle_list_resource_templates() -> list[types.ResourceTemplate]: # Implementation

3. Define notification handlers if needed: @server.progress_notification() async def handle_progress( progress_token: str | int, progress: float, total: float | None ) -> None: # Implementation

4. Run the server: async def main(): async with mcp.server.stdio.stdio_server() as (read_stream, write_stream): await server.run( read_stream, write_stream, InitializationOptions( server_name="your_server_name", server_version="your_version", capabilities=server.get_capabilities( notification_options=NotificationOptions(), experimental_capabilities={}, ), ), )

   asyncio.run(main())

The Server class provides methods to register handlers for various MCP requests and notifications. It automatically manages the request context and handles incoming messages from the client. """