Latest insights, tutorials, and updates from the Model Context Protocol team
The ai-engineering-from-scratch MCP Client in Python is a structured learning and build-it-yourself resource for AI engineering. It offers 503 lessons across 20 phases to help developers learn, build, and ship AI systems, leveraging MCP for contextual communication.
casdoor is an open-source, Go-based Identity and Access Management (IAM) and LLM gateway, specifically designed with an AI-first approach. It acts as an MCP Client, providing robust authentication and authorization for AI applications and agent-to-agent communication. Developers building AI systems needing comprehensive identity services and MCP integration will find it particularly useful.
Qwen-Agent is a Python-based agent framework built on Qwen>=3.0, designed to integrate various capabilities including Function Calling, Code Interpreter, and RAG. It functions as an MCP Client, enabling developers to orchestrate external MCP servers like memory, filesystem, and SQLite for enhanced agent functionality.
The free-llm-api-resources MCP Server, a Python project, provides a curated list of free LLM inference resources accessible via API. It solves the problem of discovering legitimate, no-cost LLM API access, making it essential for developers integrating LLMs into prototypes or budget-constrained applications.
SurfSense is an open-source NotebookLM alternative designed for open web research with live data. It provides a suite of connectors as native tools, accessible to your agents via its MCP server. Developers can integrate SurfSense with platforms like Claude or Cursor to empower AI agents with real-time web research capabilities.
posthog is a Python-based MCP Client for building self-driving products, offering AI observability, analytics, and developer tools. It captures agent context for diagnosing problems and uncovering opportunities, steerable from Slack, web, desktop, or the MCP.