🧠 Model Context Protocol (MCP) The Universal Connector for AI Agents

The Model Context Protocol (MCP) from Anthropic is an open standard for connecting AI models to tools, APIs, and data sources in a structured and secure way. It enables developers to build powerful AI agents that can interact with external systems such as databases, services, or internal company tools without custom, hardcoded integrations.

🔍 What MCP Does

Think of MCP as a universal adapter for AI agents. It defines how external tools (like APIs, databases, file systems, etc.) can expose their functionality to a language model (LLM) using a standard JSON-RPC 2.0 based interface.

🧠 Why It Matters

MCP enables

• Tool use by AI agents: Agents can make API calls, look up documents, take actions, and more.
• Extensibility: Tools can be registered dynamically, and models can reason over which tools to use and when.
• Interoperability: One MCP compliant tool server can be used across any system that supports MCP.

🧱 What it´s not

• A framework for building agents
• A fundamental change to how agents work
• A way to code agents

🔌 MCP core Concepts

The three Components

1. Host is an LLM app like claude or agent architecture
2. MCP Client lives inside Host and connects 1:1 MCP Server
3. MCP Server provides tools, context and prompts

🛠️ Architecture

📡 Transport mechanisms

1. Stdio spawns a process and communicates via standard input/output
2. SSE uses HTTPS connections with streaming

MCP Marketplace

• 🔗 mcp.so
• 🔗 glama.ai
• 🔗 smithery.ai
• 🔗 huggingface.co

🔐 Security Model

MCP supports

• Permission scoped tool access
• Per user or per session authorization
• Audit logging for traceability

🌐 Real World Use Cases

• 🔎 Document search & retrieval
• 💬 Customer service AI agents accessing ticket systems
• 📊 Data analysis via SQL connectors
• 🛠️ DevOps automation using internal tools

📦 Code Example

1from dotenv import load_dotenv
2from agents import Agent, Runner, trace
3from agents.mcp import MCPServerStdio
4import os
5import asyncio
6
7
8load_dotenv(override=True)
9
10#Let's use MCP in OpenAI Agents SDK
11# 1.Create a Client
12# 2.Have it spawn a server
13# 3.Collect the tools that the server can use
14
15async def main():
16  # Create sandbox directory if it doesn't exist
17  sandbox_path = os.path.abspath(os.path.join(os.getcwd(), "sandbox"))
18  os.makedirs(sandbox_path, exist_ok=True)
19  
20  # Set up filesystem server
21  files_params = {"command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", sandbox_path]}
22
23  async with MCPServerStdio(params=files_params) as server:
24      file_tools = await server.list_tools()
25      print("\nAvailable filesystem tools:")
26      for tool in file_tools:
27          print(f"{tool.name}: {tool.description.replace('\n', ' ')}")
28
29      # Simple test with the Agent
30      instructions = """
31      You can read and write files in the sandbox directory.
32      When writing files, make sure to use markdown format.
33      """
34
35      agent = Agent(
36          name="file_handler",
37          instructions=instructions,
38          model="gpt-4o-mini",
39          mcp_servers=[server]
40      )
41      
42      with trace("write_test"):
43          result = await Runner.run(agent, "Create a simple test.md file with 'Hello World!' in it")
44          print("
45Agent result:", result.final_output)
46
47if __name__ == "__main__":
48  asyncio.run(main())

📚 Learn More

👉 Documentation: Anthropic’s official documentation on MCP