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MCP Sampling
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AI & Agentic Engineering
MCP Client Capabilities Explained: Roots, Sampling, Elicitation and Experimental Features
MCP Elicitation enables safer AI agents by requesting user input, validating decisions, and controlling high-impact MCP actions.
AI & Agentic Engineering
MCP Roots vs Resources vs Tools: 7 Powerful Data Access Patterns Every AI Engineer Must Master
MCP Roots vs Resources vs Tools explains the practical difference between scope, information, and executable capabilities in production MCP servers, including security,…
AI & Agentic Engineering
MCP Roots Explained: Powerful and Secure Filesystem Access for AI Agents
Learn MCP Roots security with 7 powerful lessons covering path validation, permissions, root changes, AI context, RAG, tools, and safe filesystem access.
AI & Agentic Engineering
MCP Sampling Explained: How Servers Request AI Model Completions
Master MCP Sampling with this complete guide covering sampling requests, AI model communication, human approval, security, governance, and production best practices.