#python #ai #authentication #authorization #claude #cursor #fastapi #llm #mcp #mcp_server #mcp_servers #modelcontextprotocol #openapi #windsurf

FastAPI-MCP is a tool that lets you easily turn your FastAPI web API endpoints into Model Context Protocol (MCP) tools, which AI agents can use directly. It requires almost no setup—just connect it to your FastAPI app, and it automatically preserves your request/response data models and documentation. It also includes built-in authentication using your existing FastAPI security methods. You can run the MCP server inside your app or separately, and it communicates efficiently using FastAPI’s ASGI interface. This makes it simple to integrate AI capabilities with your existing FastAPI services without rewriting code, saving you time and effort while keeping your API secure and well-documented[1][5].

https://github.com/tadata-org/fastapi_mcp

#python #csharp #java #javascript #javascript_applications #mcp #mcp_client #mcp_security #mcp_server #model #model_context_protocol #modelcontextprotocol #python #typescript

You can learn the Model Context Protocol (MCP), a new standard for connecting AI models with applications, through a free, open-source curriculum that includes hands-on coding examples in C#, Java, JavaScript, Python, and TypeScript. The curriculum covers basics, security, building servers and clients, advanced topics, and best practices, with multi-language support and community help via Discord. You can also join MCP Dev Days, a free online event for deep technical learning and networking. This resource helps you quickly gain practical skills to build and integrate AI tools effectively, boosting your development capabilities in AI workflows.

https://github.com/microsoft/mcp-for-beginners

#python #aws #mcp #mcp_client #mcp_clients #mcp_host #mcp_server #mcp_servers #mcp_tools #modelcontextprotocol

AWS MCP Servers use the Model Context Protocol (MCP), an open standard that connects AI tools with AWS data and services in a simple, secure way. These servers improve AI responses by providing up-to-date AWS documentation, best practices, and workflow automation for cloud development, infrastructure, and operations. You can run MCP servers locally for development or use AWS-managed remote servers for easy access and scalability. MCP servers support many AWS services like Lambda, DynamoDB, EKS, and more, helping you build, manage, and optimize AWS resources efficiently with AI assistance. Installation is easy with one-click options for popular tools like VS Code and Cursor. This makes cloud development faster, more accurate, and cost-effective.

https://github.com/awslabs/mcp