🧠 What is MCP Server?

How to Build One Using Cursor AI?

Hi Friends,
MCP (Model Context Protocol) is quickly becoming a foundational piece of the AI agent ecosystem — designed by Anthropic to let you build agents that think, remember, and act like humans in a conversation loop.

This isn't prompt engineering anymore — it's protocol-level control over memory, identity, and tool use.

Let’s unpack how MCP works, and how you can create your own MCP agent server using Anthropic's official SDK and Cursor AI.

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🤖 What is MCP?

MCP defines how Claude (or any LLM supporting MCP) communicates with your backend using structured, typed messages. It handles:

• ✅ Agent identity

• 🧠 Long-term memory

• 🔧 Tool calling

• 📦 External resource access

With MCP, your agent becomes stateful and goal-driven — remembering what you did last time, asking for tools, and updating memory as it works.

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🧰 Types of MCP Servers

Anthropic provides three patterns to run your own MCP agent backend:

1. stdio-server

Runs locally and communicates via stdin/stdout. Good for quick CLI agents or offline use.

2. http-server

Exposes a web endpoint and handles POST /mcp.handle-request calls.

3. custom

Your own setup with full control — this is where most advanced use cases land.

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🧑‍💻 How to Build an MCP Agent with Python’s FastMCP?

Let’s say you want to build a Claude-compatible MCP agent that can do math and greet people dynamically.

Here’s a working example using Anthropic's Python MCP SDK:

from mcp.server.fastmcp import FastMCP 
# Step 1: Create the MCP server mcp = FastMCP("Demo") 

# Step 2: Register tools 
@mcp.tool() 
def add(a: int, b: int) -> int:
 """Add two numbers""" 
return a + b 

# Step 3: Register a resource 
@mcp.resource("greeting://{name}") 
def get_greeting(name: str) -> str: 
"""Get a personalized greeting""" 
return f"Hello, {name}!"

That’s it. Claude can now use add() during a conversation and resolve URLs like greeting://john.

Behind the scenes, MCP routes:

• mcp.handle-request calls

• tool-use and tool-result events

• memory and identity data in a clean, extensible way.

🎯 This structure is great for production-grade agents, and Cursor AI helps you:

• Scaffold this repo

• Add strongly typed tools and resource endpoints

• Run prompt-driven testing of your MCP agent

• Add logging, memory stores, and even hybrid Claude/OpenAI agents

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🔥 Real-World Use Cases

📊 Sales Agents — persist pipeline state, prioritize leads, auto-summarize calls
📁 Semantic File Browsers — Claude acts on custom file://, db://, log:// URLs
🧰 Agent Toolchains — offload planning + function calling to the LLM, execute locally

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📚 Books + Resources You Should Bookmark

• Anthropic’s Official MCP Docs — protocol format and server patterns

• Python mcp SDK on PyPI — FastMCP and decorators

• 📖 Designing Agents That Work by Michael Wooldridge — mental model for agent theory

• 💻 Cursor AI — use it to build, refactor, and test MCP agents with native Claude support

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“You don’t get real intelligence without continuity. Context is memory. Protocols like MCP make it possible.”
— Inspired by On Intelligence by Jeff Hawkins

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Until next time,
— Jenifer 🛠️🧠

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