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.
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That's a wrap!
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@akshay_pachaar ✔️
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