Introduction to Model Context Protocol (MCP) by Anthropic

Overview

• Anthropic introduces a new protocol called Model Context Protocol (MCP).
• Similar to AI models using plugins or actions like GPT by OpenAI.
• MCP connects clients, servers, and large language models.

Demonstration

• Example given: Fetching weather for London using a custom application.
• AI tool (Claude) relies on external application to fetch real-time weather data.
• Application acts like an endpoint, receiving city names and returning weather information.

Technical Details

• MCP server: Your custom application.
• MCP client: Cloud (Claude's platform).
• Integration involves setting up API endpoints on your application.

Implementation Steps

Prerequisites

• Install Cloud for desktop (platform-specific downloads available).
• Ensure Python and uv package manager are installed.

Creating MCP Server

1. Setup Notes Application

   • Use uvx create mcp server command to initiate.
   • Provide project name (e.g., notes), description, version.
   • Confirm paths and installation prompts.
   • Install necessary packages using uv sync all extras.
   • Navigate and configure application in Cloud.app.

2. Key Files and Functions

   • Locate source folder within the project path.
   • Understand server.py structure: function definitions, decorators.
   • Example tool: Add Note with functionalities for adding, updating, deleting notes.
   • Functionality expands by storing/retrieving data (e.g., databases).

Creating Weather Application

1. Setup Weather Application

   • Similar setup process as Notes application.
   • Integrate OpenWeather API for real-time data.
   • Key function: fetchWeather to get weather based on city name.

2. Function Decorators

   • list tools: Defines available functions (e.g., get forecast).
   • call tool: Executes corresponding actions based on requests.
   • Optional: list resource and read resource decorators for additional functionalities.

Additional Information

• MCP operates locally on desktop app, not yet in web cloud app.
• Tool calls include: get forecast, add note, artifacts (JavaScript analysis).
• MCP is now open-source, enabling custom tool integration.

Conclusion

• Encouraged to explore additional features such as Cloud Context Caching.
• Additional resources and code are provided for further exploration.
• MCP has potential for extensive customization and application development.