How to Give an LLM Real-Time Information: APIs, RAG, and MCP
Choose between live APIs, document retrieval, and MCP based on the information your assistant needs. Design for timestamps, permissions, stale caches, and unavailable sources.
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6 articles found with this tag
MCP vs RAG vs Function Calling: Differences and When to Use Each
MCP connects applications to tools and data. RAG retrieves evidence for answers. Function calling lets models request execution. Compare them through a reimbursement policy assistant.
Read articleMCP Clients Explained – How Models Consume Context Safely
MCP servers expose context and capabilities, but MCP clients determine how models consume them safely. This article explains the role of an MCP client, how it brokers access between models and systems, and why client-side design is critical for reliability, security, and correctness.
Read articleBuilding Your First MCP Server: A Practical Python Walkthrough
Build a local Python MCP server with the official SDK, expose a tool, resource, and prompt, then verify each through a client without an LLM or API key.
Read articleMCP Core Concepts Explained – Resources, Tools, and Prompts
After understanding why MCP exists, the next step is mastering its core building blocks. This article explains MCP resources, tools, and prompts in depth, clarifies their responsibilities, and shows how they work together to create safe, scalable LLM systems.
Read articleIntroduction to MCP – Why Context Is the New Infrastructure
Large Language Models are powerful, but without structured context they quickly become unreliable. This article introduces the Model Context Protocol (MCP), explains why context management is now a core infrastructure concern, and shows how MCP enables scalable, safe, and maintainable AI systems.
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