This article details authentication and authorization mechanisms within the Model Context Protocol (MCP), covering transport layers like stdio and Streamable HTTP, OAuth flows, and security considerations for MCP servers.
The article discusses the evolution from RAG (Retrieval-Augmented Generation) to 'context engineering' in the field of AI, particularly with the rise of agents. It explores how companies like Contextual AI are building platforms to manage context for AI agents and highlights the shift from prompt engineering to managing the entire context state.
A guide to supercharging Claude Code with Skills and the Model Context Protocol (MCP), including running Claude Code in an IDE like Cursor or VS Code. It covers setting up Skills, connecting to MCP servers, and combining both for powerful workflows.
MCP Apps are now live as an official MCP extension, allowing tools to return interactive UI components directly in conversations. This enables richer experiences like dashboards, forms, and visualizations within MCP clients such as Claude, Goose, Visual Studio Code, and ChatGPT.
This post breaks down why MCP servers fail, six best practices for building ones that work, and how Skills and MCP complement each other. It emphasizes designing MCP servers as user interfaces for AI agents, focusing on outcomes, flattened arguments, clear instructions, curation, discoverable naming, and pagination.
* **Focus on Outcomes, Not Operations:** Instead of exposing granular API endpoints as tools, create high-level tools that deliver the *result* the agent needs.
* **Flatten Arguments:** Use simple, typed arguments instead of complex nested structures.
* **Instructions are Context:** Leverage docstrings and error messages to provide clear guidance to the agent.
* **Curate Ruthlessly:** Limit the number of tools exposed and focus on essential functionality.
* **Name Tools for Discovery:** Use a consistent naming convention (service_action_resource) to improve discoverability.
* **Paginate Large Results:** Avoid overwhelming the agent with large datasets; use pagination with metadata.
* **Focus on Outcomes, Not Operations:** Instead of exposing granular API endpoints as tools, create high-level tools that deliver the *result* the agent needs.
* **Flatten Arguments:** Use simple, typed arguments instead of complex nested structures.
* **Instructions are Context:** Leverage docstrings and error messages to provide clear guidance to the agent.
* **Curate Ruthlessly:** Limit the number of tools exposed and focus on essential functionality.
* **Name Tools for Discovery:** Use a consistent naming convention (service_action_resource) to improve discoverability.
* **Paginate Large Results:** Avoid overwhelming the agent with large datasets; use pagination with metadata.
Zhipu AI has released GLM-4.7-Flash, a 30B-A3B MoE model designed for efficient local coding and agent applications. It offers strong coding and reasoning performance with a 128k token context length and supports English and Chinese.
SimpleMem addresses the challenge of efficient long-term memory for LLM agents through a three-stage pipeline grounded in Semantic Lossless Compression. It maximizes information density and token utilization, achieving superior F1 scores with minimal token cost.
Exploring secure environments for testing and running AI agent code, including options like Docker, online IDEs, and dedicated platforms.
Vercel has open-sourced bash-tool, a Bash execution engine for AI agents, enabling them to run filesystem-based commands to retrieve context for model prompts. It allows agents to handle large local contexts without embedding entire files, by running shell-style operations like find, grep, and jq.
mcp-cli is a lightweight CLI that enables dynamic discovery of MCP servers, reducing token consumption and making tool interactions more efficient for AI coding agents.
