#python #agents #ai #ai_agents #llm #llms #mcp #model_context_protocol #python

The Model Context Protocol (MCP) is a standard way for AI agents to connect with different tools and data sources, making it much easier to build powerful AI applications without writing custom code for each integration[2][5]. The mcp-agent framework uses MCP to let you quickly create agents that can do things like read files, fetch web pages, or manage emails, and you can combine these agents in flexible ways to handle complex tasks. This means you can focus on what you want your AI to do, while mcp-agent takes care of connecting to the right tools and managing the workflow, saving you time and effort[3][5].

https://github.com/lastmile-ai/mcp-agent

#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

#go #databases #genai #llms #mcp

The MCP Toolbox for Databases helps developers connect AI agents to databases more easily and securely. It simplifies the process by handling complex tasks like connection pooling and authentication, allowing you to integrate databases with AI agents using minimal code. This toolbox supports the Model Context Protocol (MCP), which standardizes how AI interacts with external tools. By using MCP Toolbox, you can automate database tasks, query databases using natural language, and generate context-aware code, all of which save time and improve development efficiency.

https://github.com/googleapis/genai-toolbox

#other #clients #mcp

The Model Context Protocol (MCP) is an open standard that lets AI models easily and securely connect to different data sources and tools, making it much simpler for developers to build smart apps that can access files, databases, and APIs without custom code for each one[2][3][4]. There are many free and easy-to-use MCP clients—like desktop apps, web apps, and command-line tools—that let you quickly add new AI features and automate tasks, so you can get more done with less effort and technical hassle. This means you can use AI to help with coding, data analysis, and daily work, all while keeping your data safe and your setup flexible[2][3][4].

https://github.com/punkpeye/awesome-mcp-clients

#rust #agent #ai #amazon_q #cli #linux #llm #macos #mcp #open_source #productivity #rust #shell #terminal #typescript

Amazon Q CLI is a powerful tool that lets you interact with AWS and your development environment using natural language right from your terminal. It helps you write code, run commands, and manage AWS resources faster by understanding your context and providing smart suggestions, autocompletion, and even translating plain English into shell commands. It supports multi-turn conversations, so you can ask follow-up questions and get real-time help without leaving the command line. This boosts your productivity by simplifying complex tasks, reducing errors, and speeding up development workflows, making it easier to manage projects and infrastructure efficiently[1][2][3].

https://github.com/aws/amazon-q-developer-cli

#go #github #mcp #mcp_server

The GitHub MCP Server helps developers by connecting AI tools directly to GitHub. This allows AI assistants to manage issues, pull requests, analyze code, and automate workflows using natural language commands. It simplifies tasks like creating pull requests, reviewing code changes, and monitoring CI/CD pipelines. By automating these tasks, developers can focus more on coding and problem-solving, making their work more efficient and productive.

https://github.com/github/github-mcp-server

#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

#typescript #agentic_ai #agents #ai #claude #copilot #cursor #git #llm #mcp

GitMCP is a free, open-source service that connects AI assistants to any GitHub project’s latest documentation and code using the Model Context Protocol (MCP). This means your AI can access up-to-date, accurate information directly from the source, reducing mistakes and hallucinations when coding or asking questions about libraries, even new or niche ones. You just add a GitMCP URL for your chosen GitHub repo to your AI tool, and it fetches relevant docs and code smartly without setup hassle. This helps you get reliable code examples and API usage instantly, improving your coding efficiency and accuracy. It’s private, easy to use, and works with many AI assistants.

https://github.com/idosal/git-mcp

#python #agents #ai #api_gateway #asyncio #authentication_middleware #devops #docker #fastapi #federation #gateway #generative_ai #jwt #kubernetes #llm_agents #mcp #model_context_protocol #observability #prompt_engineering #python #tools

The MCP Gateway is a powerful tool that unifies different AI service protocols like REST and MCP into one easy-to-use endpoint. It helps you manage multiple AI tools and services securely with features like authentication, retries, rate-limiting, and real-time monitoring through an admin UI. You can run it locally or in scalable cloud environments using Docker or Kubernetes. It supports various communication methods (HTTP, WebSocket, SSE, stdio) and offers observability with OpenTelemetry for tracking AI tool usage and performance. This gateway simplifies connecting AI clients to diverse services, making development and management more efficient and secure.

https://github.com/IBM/mcp-context-forge

#php #agent #agi #ai #gpt #llm #low_code #mcp #no_code #sandbox #workflow

Magic is an open-source AI platform that helps businesses quickly build and use AI tools to boost productivity by up to 100 times. It offers a complete set of AI products, including a smart AI agent for complex tasks, an AI-powered chat system for team communication, and a visual tool to create AI workflows without coding. These tools work together to improve decision-making, automate tasks, and enhance collaboration securely within organizations. You can try Magic via cloud services or self-host it, making it flexible and powerful for different business needs. This platform saves time, improves efficiency, and supports smarter teamwork.

https://github.com/dtyq/magic