Will Jackson

MCP is getting so good! Most developers think MCP is just another tool calling standard, but that’s just scratching the surface. Here's what makes MCP powerful (explained with examples): Unlike simple tool calling, MCP creates a two-way communication between your AI apps and servers. Let me break down the 6 core primitives that make this possible: Starting with the client! A client offers 3 capabilities: sampling, roots, and elicitation. 1️⃣ Sampling The server can ask the client to generate LLM completions, but the client still controls permissions and safety. Example: A travel server asks the LLM to pick the best flight from a list. 2️⃣ Roots Clients define what files the server can access. Secure, sandboxed, and scoped. Example: A server for booking travel may be given access to a specific directory, from which it can read a user’s calendar. 3️⃣ Elicitations Servers can request user input mid-task, in a structured way. Example: A server booking travel may ask for the user’s preferences on airplane seats, room type or their contact number to finalise a booking. Moving on, let's talk about the MCP server now. Serve also exposes 3 capabilities: tools, resources, and prompts 4️⃣ Tools Controlled by the model, tools are functions that do things: write to DBs, trigger logic, send emails, etc. Examples: - search flights - send messages - create calendar events 5️⃣ Resources Controlled by the app, resources are the passive, read-only data like files, calendar, KBs, APIs. Examples: - retrieve docs - read calendars - access knowledge bases 6️⃣ Prompts Controlled by the user, prompts are pre-built instruction templates that guide how the LLM uses tools/resources. Examples: - plan a vacation - draft an email - summarize my meetings 👉 If you're interested in learning more about MCP, let me know in the comments. I've created a FREE illustrated guidebook with over 10 projects, along with a YouTube playlist for those who prefer videos. _____ That's a wrap! If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning