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Seenivasa Ramadurai
Seenivasa Ramadurai

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MCP – Model Context Protocol: The New Standard for AI Data Connectivity

In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have revolutionized how we approach tasks like coding, question answering, and content creation. However, these LLMs do not have access to external data sources to get real-time and domain-specific data. Even though we use RAG and tools to update their knowledge, a significant challenge has persisted: each AI system typically requires a custom integration approach, with specific APIs, databases, or unique setups. This fragmentation has limited the ability of LLM-powered agents to access external information such as your files, emails, project documents, and APIs.

Introducing Model Context Protocol (MCP)

Recognizing this challenge, Anthropic has developed the Model Context Protocol (MCP) – an open standard for AI that enables models to dynamically connect with tools, files, and APIs.

Think of MCP as the USB-C for AI connectivity – a universal interface for feeding context to AI systems, allowing them to seamlessly integrate with external data sources. This breakthrough means developers and end users no longer need different interfaces to connect to various AI systems, whether they're working with an AI-powered IDE, chatbot, agent, or application. As a result, retrieving and acting on real-time information is becoming more standardized than ever before.

MCP servers offer separation of concerns from AI agents and LLM-powered applications, allowing tools and functionality to be integrated with any AI assistant or agent. The protocol promises a unified standard to connect any tool or service.

How MCP Works

MCP utilizes a client-server model with two main components:

  • MCP Server: Provides structured context to AI models, functioning as a database, file system, cloud services data storage, or API that AI can query.
  • MCP Host/Client: The AI application (such as Claude for Desktop, Cursor IDE, or Windsurf) or custom AI-powered applications that request context from the MCP Server.

Practical Applications

In this blog, we'll explore three different MCP Server implementations:

  1. Database Operations – Query, delete, update, and insert user profile data in an SQLite database
  2. File System Access – List, delete, and rename files from a given directory
  3. Real-time Data with SSE (Server-Sent Events) – Expose endpoints to retrieve all cities and get weather information for specific cities
  4. Cloud Storage Integration – List all file or blob names from Azure storage

We'll then build custom AI agents or chatbots to query real-time information from these MCP servers, demonstrating the versatility and power of this new protocol.

Architecture

Image description

Multi MCP Servers and MCP Client Implementation

1. FastMCP server (SSE) 

import asyncio
import json
import random
from datetime import datetime
from typing import List, AsyncGenerator
from mcp.server.fastmcp import FastMCP


# Cities from India and USA
cities = {
    "India": [
        "Mumbai", "Delhi", "Bangalore", "Hyderabad", "Chennai", 
        "Kolkata", "Pune", "Ahmedabad", "Jaipur", "Lucknow"
    ],
    "USA": [
        "New York", "Los Angeles", "Chicago", "Houston", "Phoenix", 
        "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose"
    ]
}

# Temperature ranges for different cities (°C)
temp_ranges = {
    "India": (-5, 45),   # India can get very hot in summer
    "USA": (-20, 40)     # USA can get colder in northern states
}

mcp = FastMCP("weather", port=5023, host="localhost")


async def weather_generator(location: str | None = None) -> AsyncGenerator[str, None]:
    """Generate simulated weather data for cities in India and USA."""
    weather_conditions = ["Sunny", "Partly Cloudy", "Cloudy", "Rainy", "Thunderstorm", "Windy", "Snowy", "Foggy"]

    while True:
        # Generate data for a random city from each country
        india_city = random.choice(cities["India"])
        usa_city = random.choice(cities["USA"])

        city_data = []

        # If location is specified, only return data for that city's country
        if location:
            target_cities = []
            if location.upper() in [city.upper() for city in cities["India"]]:
                target_cities = [location]
                temp_range = temp_ranges["India"]
            elif location.upper() in [city.upper() for city in cities["USA"]]:
                target_cities = [location]
                temp_range = temp_ranges["USA"]
            else:
                # If location not found, use default behavior
                target_cities = [india_city, usa_city]

            for city in target_cities:
                country = "India" if city in cities["India"] else "USA"
                temp_range = temp_ranges[country]

                city_data.append({
                    "country": country,
                    "city": city,
                    "timestamp": datetime.now().isoformat(),
                    "temperature": round(random.uniform(*temp_range), 1),
                    "condition": random.choice(weather_conditions),
                    "humidity": random.randint(20 if country == "USA" else 30, 95 if country == "USA" else 100),
                    "wind_speed": round(random.uniform(0, 30 if country == "USA" else 25), 1)
                })
        else:
            # Default behavior - one city from each country
            india_data = {
                "country": "India",
                "city": india_city,
                "timestamp": datetime.now().isoformat(),
                "temperature": round(random.uniform(*temp_ranges["India"]), 1),
                "condition": random.choice(weather_conditions),
                "humidity": random.randint(30, 100),
                "wind_speed": round(random.uniform(0, 25), 1)
            }
            city_data.append(india_data)

            usa_data = {
                "country": "USA",
                "city": usa_city,
                "timestamp": datetime.now().isoformat(),
                "temperature": round(random.uniform(*temp_ranges["USA"]), 1),
                "condition": random.choice(weather_conditions),
                "humidity": random.randint(20, 95),
                "wind_speed": round(random.uniform(0, 30), 1)
            }
            city_data.append(usa_data)

        # Format as SSE message
        yield f"data: {json.dumps(city_data)}\n\n"

        # Wait before sending next update
        await asyncio.sleep(3)  # Update every 3 seconds

@mcp.tool("stream_weather")
async def stream_weather(location: str | None = None):
    """SSE endpoint for streaming weather updates from India and USA cities.

    Args:
        location: Optional city name to filter weather data for a specific city
    """
    return weather_generator(location)

@mcp.tool()
async def get_cities():
    """Get the list of all available cities grouped by country."""
    india_cities = "\n".join(f"- {city}" for city in cities["India"])
    usa_cities = "\n".join(f"- {city}" for city in cities["USA"])

    return f"""Cities in India:
{india_cities}

Cities in USA:
{usa_cities}"""

if __name__ == "__main__":
    mcp.run(transport="sse")
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2.MCP Server exposes Database SQLite data and FileSystems 

import os
import sqlite3
from mcp.server.fastmcp import FastMCP
from mcp.types import ImageContent
from typing import List, Dict, Optional
from PIL import Image as PILImage
from io import BytesIO
import base64

mcp = FastMCP("Sreeni's File and Friend Management System")

# Initialize SQLite database
def init_db():
    conn = sqlite3.connect('sreeni_friends.db')
    c = conn.cursor()
    c.execute('''
        CREATE TABLE IF NOT EXISTS SreeniFriend (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            first_name TEXT NOT NULL,
            last_name TEXT NOT NULL,
            email TEXT UNIQUE NOT NULL,
            phone TEXT,
            city TEXT,
            state TEXT,
            country TEXT
        )
    ''')

    # Insert sample data
    sample_data = [
        ('Pradeep', 'Sethi', 'pradeep.sethi@email.com', '123-456-7890', 'Bangalore', 'Karnataka', 'India'),
        ('Harithosh', 'Tewari', 'harithosh.tewari@email.com', '987-654-3210', 'Delhi', 'Delhi', 'India'),
        ('Manohar', 'Reddy', 'manohar.reddy@email.com', '555-123-4567', 'Hyderabad', 'Telangana', 'India'),
        ('Amith', 'Shah', 'amith.shah@email.com', '444-555-6666', 'Mumbai', 'Maharashtra', 'India'),
        ('Pankaj', 'Jainani', 'pankaj.jainani@email.com', '777-888-9999', 'Jaipur', 'Rajasthan', 'India'),
        ('Prodip', 'Shah', 'prodip.shah@email.com', '111-222-3333', 'Kolkata', 'West Bengal', 'India'),
        ('Subburaj', 'Sundaraj', 'subburaj.sundaraj@email.com', '999-888-7777', 'Chennai', 'Tamil Nadu', 'India'),
        ('Vinny', 'Davi', 'vinny.davi@email.com', '222-333-4444', 'Chennai', 'Tamil Nadu', 'India'),
        ('Parthasarathy', 'Gurusamy', 'parthasarathy.g@email.com', '333-444-5555', 'Bangalore', 'Karnataka', 'India'),
        ('Jayachandran', 'Ramadurai', 'jayachandran.r@email.com', '444-555-6666', 'Chennai', 'Tamil Nadu', 'India')
    ]

    try:
        c.executemany('''
            INSERT OR IGNORE INTO SreeniFriend 
            (first_name, last_name, email, phone, city, state, country)
            VALUES (?, ?, ?, ?, ?, ?, ?)
        ''', sample_data)
        conn.commit()
    finally:
        conn.close()

# Initialize database on startup
init_db()

@mcp.tool()
def add_friend(first_name: str, last_name: str, email: str, phone: str = "", 
               city: str = "", state: str = "", country: str = "") -> Dict:
    """Add a new friend to the database."""
    try:
        conn = sqlite3.connect('sreeni_friends.db')
        c = conn.cursor()
        c.execute('''
            INSERT INTO SreeniFriend 
            (first_name, last_name, email, phone, city, state, country)
            VALUES (?, ?, ?, ?, ?, ?, ?)
        ''', (first_name, last_name, email, phone, city, state, country))
        conn.commit()
        return {"message": "Friend added successfully", "id": c.lastrowid}
    except sqlite3.IntegrityError:
        return {"error": "Email already exists"}
    except Exception as e:
        return {"error": str(e)}
    finally:
        conn.close()

@mcp.tool()
def get_all_friends() -> List[Dict]:
    """Get all friends from the database."""
    try:
        conn = sqlite3.connect('sreeni_friends.db')
        c = conn.cursor()
        c.execute('SELECT * FROM SreeniFriend')
        columns = [description[0] for description in c.description]
        friends = [dict(zip(columns, row)) for row in c.fetchall()]
        return friends
    except Exception as e:
        return {"error": str(e)}
    finally:
        conn.close()

@mcp.tool()
def search_friends(query: str) -> List[Dict]:
    """Search friends by name, email, city, or country."""
    try:
        conn = sqlite3.connect('sreeni_friends.db')
        c = conn.cursor()
        search_query = f"%{query}%"
        c.execute('''
            SELECT * FROM SreeniFriend 
            WHERE first_name LIKE ? 
            OR last_name LIKE ? 
            OR email LIKE ?
            OR city LIKE ?
            OR country LIKE ?
        ''', (search_query, search_query, search_query, search_query, search_query))
        columns = [description[0] for description in c.description]
        friends = [dict(zip(columns, row)) for row in c.fetchall()]
        return friends
    except Exception as e:
        return {"error": str(e)}
    finally:
        conn.close()

@mcp.tool()
def update_friend(email: str, updates: Dict) -> Dict:
    """Update friend information by email.

    Args:
        email: Email of the friend to update
        updates: Dictionary of fields to update
    """
    valid_fields = {'first_name', 'last_name', 'phone', 'city', 'state', 'country'}
    update_fields = {k: v for k, v in updates.items() if k in valid_fields and v}

    if not update_fields:
        return {"error": "No valid fields to update"}

    try:
        conn = sqlite3.connect('sreeni_friends.db')
        c = conn.cursor()

        set_clause = ', '.join(f"{field} = ?" for field in update_fields)
        query = f"UPDATE SreeniFriend SET {set_clause} WHERE email = ?"

        values = list(update_fields.values()) + [email]
        c.execute(query, values)
        conn.commit()

        if c.rowcount > 0:
            return {"message": "Friend updated successfully"}
        return {"error": "Friend not found"}
    except Exception as e:
        return {"error": str(e)}
    finally:
        conn.close()

@mcp.tool()
def delete_friend(email: str) -> Dict:
    """Delete a friend by email."""
    try:
        conn = sqlite3.connect('sreeni_friends.db')
        c = conn.cursor()
        c.execute('DELETE FROM SreeniFriend WHERE email = ?', (email,))
        conn.commit()

        if c.rowcount > 0:
            return {"message": "Friend deleted successfully"}
        return {"error": "Friend not found"}
    except Exception as e:
        return {"error": str(e)}
    finally:
        conn.close()

@mcp.tool()
def get_friends_by_country(country: str) -> List[Dict]:
    """Get all friends from a specific country."""
    try:
        conn = sqlite3.connect('sreeni_friends.db')
        c = conn.cursor()
        c.execute('SELECT * FROM SreeniFriend WHERE country = ?', (country,))
        columns = [description[0] for description in c.description]
        friends = [dict(zip(columns, row)) for row in c.fetchall()]
        return friends
    except Exception as e:
        return {"error": str(e)}
    finally:
        conn.close()

@mcp.tool()
def read_file(path: str) -> str:
    with open(path, "r") as f:
        return f.read()


@mcp.tool()
def list_files(path: str) -> list[str]:
    return os.listdir(path)


@mcp.tool()
def create_file(path: str, content: str):
    with open(path, "w") as f:
        f.write(content)

@mcp.tool()
def delete_file(path: str):
    os.remove(path)

@mcp.tool()
def rename_file(old_path: str, new_path: str):
    os.rename(old_path, new_path)

@mcp.resource("config://app")
def get_config() -> Dict:
    """Static configuration data"""
    return {
        "name": "Sreeni's Friend Management System",
        "version": "1.0.0",
        "description": "A system to manage friends and their contact information",
        "supported_countries": ["India", "USA", "UK"],
        "max_friends": 1000
    }

@mcp.resource("users://{user_id}/profile")
def get_user_profile(user_id: str) -> Dict:
    """Get user profile from the friends database.

    Args:
        user_id: Email of the friend to fetch profile for

    Returns:
        Dict containing the friend's profile information
    """
    try:
        conn = sqlite3.connect('sreeni_friends.db')
        c = conn.cursor()
        c.execute('SELECT * FROM SreeniFriend WHERE email = ?', (user_id,))
        columns = [description[0] for description in c.description]
        row = c.fetchone()

        if row:
            profile = dict(zip(columns, row))
            # Add additional profile information
            profile.update({
                "role": "friend",
                "permissions": ["view", "contact"],
                "added_date": "2024-03-20",
                "status": "active",
                "connection_type": "direct"
            })
            return profile
        return {
            "error": "Profile not found",
            "user_id": user_id
        }
    except Exception as e:
        return {"error": str(e)}
    finally:
        conn.close()


@mcp.tool()
def create_thumbnail(image_path: str) -> ImageContent:
    """Create a thumbnail from an image"""
    try:
        img = PILImage.open(image_path)
        img.thumbnail((100, 100))
        # Convert to PNG bytes using BytesIO
        img_byte_arr = BytesIO()
        img.save(img_byte_arr, format='PNG')
        img_byte_arr = img_byte_arr.getvalue()
        return ImageContent(type='image', data=base64.b64encode(img_byte_arr).decode('utf-8'), mimeType='image/png')
    except Exception as e:
        print(f"Error creating thumbnail: {e}")
        return ImageContent(type='error', data=str(e), mimeType='text/plain')


if __name__ == "__main__":
    print("Starting MCP server with Friend Management System...")
    mcp.run(transport="stdio")
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3. MCP Custom Client 

from  langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import AzureChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
import asyncio
import os
load_dotenv()
# Azure OpenAI Configuration
AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
AZURE_OPENAI_KEY = os.getenv("AZURE_OPENAI_KEY")
AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT", "gpt-4")
AZURE_OPENAI_MODEL = os.getenv("AZURE_OPENAI_MODEL", "gpt-4")
AZURE_OPENAI_VERSION = os.getenv("AZURE_OPENAI_VERSION", "2023-05-15")

llm = AzureChatOpenAI(
                        azure_deployment="gpt-4o-mini",
                        azure_endpoint=AZURE_OPENAI_ENDPOINT,
                        api_key=AZURE_OPENAI_KEY,
                        api_version=AZURE_OPENAI_VERSION,
                        temperature=0,
                    )

async def main():
    async with MultiServerMCPClient(
        {
            "math": {
                "command": "python",
                "args": ["mcpserver.py"],
                "transport": "stdio",
            },
            "weather": {
                "url": "http://localhost:5023/sse",
                "transport": "sse",
            },
            "azure_blob": {
                "command": "python",
                "args": ["azure_blob.py"],
                "transport": "stdio",
            }
        }
    ) as client:
        try:
            tools = client.get_tools()
            agent = create_react_agent(
                model=llm,
                tools=tools,

            )

            print("\nAgent initialized successfully. Type 'exit' to quit.")

            while True:
                try:
                    query = input("\nEnter a query: ").strip()

                    if query.lower() == 'exit':
                        print("Exiting...")
                        break

                    if not query:
                        continue

                    result = await agent.ainvoke({
                        "messages": [{"role": "user", "content": query}],
                        "agent_scratchpad": ""
                    })

                    for res in result["messages"]:
                        print(res.pretty_print())

                    if isinstance(result, dict):
                        response = result.get('output', str(result))
                    else:
                        response = str(result)

                    print("\nAgent response:")
                    print(response)

                except KeyboardInterrupt:
                    print("\nReceived interrupt, shutting down...")
                    break
                except Exception as e:
                    print(f"\nError processing query: {e}")

        except Exception as e:
            print(f"Error initializing agent: {e}")

if __name__ == "__main__":
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nShutting down gracefully...")
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4.MCP Client & Host Invoking MCPServer and Response or output

(sreenisub) (base) sreenir@Seenivasaragavans-MacBook-Pro sreeni-sup-agent % python multi_server_client.py
Processing request of type ListToolsRequest
Processing request of type ListToolsRequest

Agent initialized successfully. Type 'exit' to quit.

Enter a query: List all friends
Processing request of type CallToolRequest
================================ Human Message =================================

List all friends
None
================================== Ai Message ==================================
Tool Calls:
get_all_friends (call_xHe7eeKgZXPhUon9k6n0mHRH)
Call ID: call_xHe7eeKgZXPhUon9k6n0mHRH
Args:
None
================================= Tool Message =================================
Name: get_all_friends

["{\"id\": 1, \"first_name\": \"Pradeep\", \"last_name\": \"Sethi\", \"email\": \"pradeep.sethi@email.com\", \"phone\": \"123-456-7890\", \"city\": \"Boston\", \"state\": \"MA\", \"country\": \"USA\"}", "{\"id\": 2, \"first_name\": \"Harithosh\", \"last_name\": \"Tewari\", \"email\": \"harithosh.tewari@email.com\", \"phone\": \"987-654-3210\", \"city\": \"Delhi\", \"state\": \"Delhi\", \"country\": \"India\"}", "{\"id\": 3, \"first_name\": \"Manohar\", \"last_name\": \"Reddy\", \"email\": \"manohar.reddy@email.com\", \"phone\": \"555-123-4567\", \"city\": \"Hyderabad\", \"state\": \"Telangana\", \"country\": \"India\"}", "{\"id\": 4, \"first_name\": \"Amith\", \"last_name\": \"Shah\", \"email\": \"amith.shah@email.com\", \"phone\": \"444-555-6666\", \"city\": \"Mumbai\", \"state\": \"Maharashtra\", \"country\": \"India\"}", "{\"id\": 5, \"first_name\": \"Pankaj\", \"last_name\": \"Jainani\", \"email\": \"pankaj.jainani@email.com\", \"phone\": \"777-888-9999\", \"city\": \"Jaipur\", \"state\": \"Rajasthan\", \"country\": \"India\"}", "{\"id\": 6, \"first_name\": \"Prodip\", \"last_name\": \"Shah\", \"email\": \"prodip.shah@email.com\", \"phone\": \"111-222-3333\", \"city\": \"Kolkata\", \"state\": \"West Bengal\", \"country\": \"India\"}", "{\"id\": 7, \"first_name\": \"Subburaj\", \"last_name\": \"Sundaraj\", \"email\": \"subburaj.sundaraj@email.com\", \"phone\": \"999-888-7777\", \"city\": \"Chennai\", \"state\": \"Tamil Nadu\", \"country\": \"India\"}", "{\"id\": 688, \"first_name\": \"Vinny\", \"last_name\": \"Davi\", \"email\": \"vinny.davi@email.com\", \"phone\": \"222-333-4444\", \"city\": \"Chennai\", \"state\": \"Tamil Nadu\", \"country\": \"India\"}", "{\"id\": 689, \"first_name\": \"Parthasarathy\", \"last_name\": \"Gurusamy\", \"email\": \"parthasarathy.g@email.com\", \"phone\": \"333-444-5555\", \"city\": \"Bangalore\", \"state\": \"Karnataka\", \"country\": \"India\"}", "{\"id\": 690, \"first_name\": \"Jayachandran\", \"last_name\": \"Ramadurai\", \"email\": \"jayachandran.r@email.com\", \"phone\": \"444-555-6666\", \"city\": \"Chennai\", \"state\": \"Tamil Nadu\", \"country\": \"India\"}"]
None
================================== Ai Message ==================================

Here is the list of all friends:

  1. Pradeep Sethi

  2. Harithosh Tewari

  3. Manohar Reddy

  4. Amith Shah

  5. Pankaj Jainani

  6. Prodip Shah

  7. Subburaj Sundaraj

  8. Vinny Davi

  9. Parthasarathy Gurusamy

  10. Jayachandran Ramadurai

Agent response:
{'messages': [HumanMessage(content='List all friends', additional_kwargs={}, response_metadata={}, id='b43aad44-1b02-4c4e-8d5d-4302cc96274e'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_xHe7eeKgZXPhUon9k6n0mHRH', 'function': {'arguments': '{}', 'name': 'get_all_friends'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 12, 'prompt_tokens': 428, 'total_tokens': 440, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ded0d14823', 'finish_reason': 'tool_calls', 'logprobs': None, 'content_filter_results': {}}, id='run-61e313cf-edd1-4a94-bc65-5ea85bba7376-0', tool_calls=[{'name': 'get_all_friends', 'args': {}, 'id': 'call_xHe7eeKgZXPhUon9k6n0mHRH', 'type': 'tool_call'}], usage_metadata={'input_tokens': 428, 'output_tokens': 12, 'total_tokens': 440, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content='["{\"id\": 1, \"first_name\": \"Pradeep\", \"last_name\": \"Sethi\", \"email\": \"pradeep.sethi@email.com\", \"phone\": \"123-456-7890\", \"city\": \"Boston\", \"state\": \"MA\", \"country\": \"USA\"}", "{\"id\": 2, \"first_name\": \"Harithosh\", \"last_name\": \"Tewari\", \"email\": \"harithosh.tewari@email.com\", \"phone\": \"987-654-3210\", \"city\": \"Delhi\", \"state\": \"Delhi\", \"country\": \"India\"}", "{\"id\": 3, \"first_name\": \"Manohar\", \"last_name\": \"Reddy\", \"email\": \"manohar.reddy@email.com\", \"phone\": \"555-123-4567\", \"city\": \"Hyderabad\", \"state\": \"Telangana\", \"country\": \"India\"}", "{\"id\": 4, \"first_name\": \"Amith\", \"last_name\": \"Shah\", \"email\": \"amith.shah@email.com\", \"phone\": \"444-555-6666\", \"city\": \"Mumbai\", \"state\": \"Maharashtra\", \"country\": \"India\"}", "{\"id\": 5, \"first_name\": \"Pankaj\", \"last_name\": \"Jainani\", \"email\": \"pankaj.jainani@email.com\", \"phone\": \"777-888-9999\", \"city\": \"Jaipur\", \"state\": \"Rajasthan\", \"country\": \"India\"}", "{\"id\": 6, \"first_name\": \"Prodip\", \"last_name\": \"Shah\", \"email\": \"prodip.shah@email.com\", \"phone\": \"111-222-3333\", \"city\": \"Kolkata\", \"state\": \"West Bengal\", \"country\": \"India\"}", "{\"id\": 7, \"first_name\": \"Subburaj\", \"last_name\": \"Sundaraj\", \"email\": \"subburaj.sundaraj@email.com\", \"phone\": \"999-888-7777\", \"city\": \"Chennai\", \"state\": \"Tamil Nadu\", \"country\": \"India\"}", "{\"id\": 688, \"first_name\": \"Vinny\", \"last_name\": \"Davi\", \"email\": \"vinny.davi@email.com\", \"phone\": \"222-333-4444\", \"city\": \"Chennai\", \"state\": \"Tamil Nadu\", \"country\": \"India\"}", "{\"id\": 689, \"first_name\": \"Parthasarathy\", \"last_name\": \"Gurusamy\", \"email\": \"parthasarathy.g@email.com\", \"phone\": \"333-444-5555\", \"city\": \"Bangalore\", \"state\": \"Karnataka\", \"country\": \"India\"}", "{\"id\": 690, \"first_name\": \"Jayachandran\", \"last_name\": \"Ramadurai\", \"email\": \"jayachandran.r@email.com\", \"phone\": \"444-555-6666\", \"city\": \"Chennai\", \"state\": \"Tamil Nadu\", \"country\": \"India\"}"]', name='get_all_friends', id='2f1e8d65-007a-4ed5-a2bf-6eda81685def', tool_call_id='call_xHe7eeKgZXPhUon9k6n0mHRH'), AIMessage(content='Here is the list of all friends:\n\n1. Pradeep Sethi\n - Email: pradeep.sethi@email.com\n - Phone: 123-456-7890\n - City: Boston\n - State: MA\n - Country: USA\n\n2. Harithosh Tewari\n - Email: harithosh.tewari@email.com\n - Phone: 987-654-3210\n - City: Delhi\n - State: Delhi\n - Country: India\n\n3. Manohar Reddy\n - Email: manohar.reddy@email.com\n - Phone: 555-123-4567\n - City: Hyderabad\n - State: Telangana\n - Country: India\n\n4. Amith Shah\n - Email: amith.shah@email.com\n - Phone: 444-555-6666\n - City: Mumbai\n - State: Maharashtra\n - Country: India\n\n5. Pankaj Jainani\n - Email: pankaj.jainani@email.com\n - Phone: 777-888-9999\n - City: Jaipur\n - State: Rajasthan\n - Country: India\n\n6. Prodip Shah\n - Email: prodip.shah@email.com\n - Phone: 111-222-3333\n - City: Kolkata\n - State: West Bengal\n - Country: India\n\n7. Subburaj Sundaraj\n - Email: subburaj.sundaraj@email.com\n - Phone: 999-888-7777\n - City: Chennai\n - State: Tamil Nadu\n - Country: India\n\n8. Vinny Davi\n - Email: vinny.davi@email.com\n - Phone: 222-333-4444\n - City: Chennai\n - State: Tamil Nadu\n - Country: India\n\n9. Parthasarathy Gurusamy\n - Email: parthasarathy.g@email.com\n - Phone: 333-444-5555\n - City: Bangalore\n - State: Karnataka\n - Country: India\n\n10. Jayachandran Ramadurai\n - Email: jayachandran.r@email.com\n - Phone: 444-555-6666\n - City: Chennai\n - State: Tamil Nadu\n - Country: India', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 522, 'prompt_tokens': 1125, 'total_tokens': 1647, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ded0d14823', 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {}}, id='run-2f03978d-971f-4ebb-9f36-7173e2a4029b-0', usage_metadata={'input_tokens': 1125, 'output_tokens': 522, 'total_tokens': 1647, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}

Enter a query: List all files from blob
Processing request of type CallToolRequest
Request URL: 'https://sreenimcp.blob.core.windows.net/sreenimcpcontainer?restype=REDACTED&comp=REDACTED'
Request method: 'GET'
Request headers:
'x-ms-version': 'REDACTED'
'Accept': 'application/xml'
'User-Agent': 'azsdk-python-storage-blob/12.25.0 Python/3.12.4 (macOS-10.16-x86_64-i386-64bit)'
'x-ms-date': 'REDACTED'
'x-ms-client-request-id': '46c554d4-fefd-11ef-ba66-a0cec87b2d15'
'Authorization': 'REDACTED'
No body was attached to the request
Response status: 200
Response headers:
'Transfer-Encoding': 'chunked'
'Content-Type': 'application/xml'
'Server': 'Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0'
'x-ms-request-id': 'eded41eb-d01e-004e-250a-9367e4000000'
'x-ms-client-request-id': '46c554d4-fefd-11ef-ba66-a0cec87b2d15'
'x-ms-version': 'REDACTED'
'Date': 'Wed, 12 Mar 2025 04:48:43 GMT'
================================ Human Message =================================

List all files from blob
None
================================== Ai Message ==================================
Tool Calls:
list_all_blobs_in_container (call_GLkCVXamBfYgm4vzsWlfZLjV)
Call ID: call_GLkCVXamBfYgm4vzsWlfZLjV
Args:
None
================================= Tool Message =================================
Name: list_all_blobs_in_container

["GenAI Boot Camp-2025.pdf", "GenAI Boot Camp.pdf", "Screenshot 2025-03-08 at 9.03.56 PM.png", "Screenshot 2025-03-08 at 9.21.06 PM.png", "Screenshot 2025-03-08 at 9.31.16 PM.png", "Screenshot 2025-03-09 at 11.17.27 AM.png", "Screenshot 2025-03-09 at 11.17.37 AM.png", "images.png", "medium-lord-krishna-photo-paper-poster-13-inch-x-19-inch-akl1958-original-imafxmetebe9s6sj.webp", "sreeni.png"]
None
================================== Ai Message ==================================

My Azure blob container

Image description

Here are the files listed in the blob:

  1. GenAI Boot Camp-2025.pdf
  2. GenAI Boot Camp.pdf
  3. Screenshot 2025-03-08 at 9.03.56 PM.png
  4. Screenshot 2025-03-08 at 9.21.06 PM.png
  5. Screenshot 2025-03-08 at 9.31.16 PM.png
  6. Screenshot 2025-03-09 at 11.17.27 AM.png
  7. Screenshot 2025-03-09 at 11.17.37 AM.png
  8. images.png
  9. medium-lord-krishna-photo-paper-poster-13-inch-x-19-inch-akl1958-original-imafxmetebe9s6sj.webp
  10. sreeni.png None

Agent response:
{'messages': [HumanMessage(content='List all files from blob', additional_kwargs={}, response_metadata={}, id='5d36dc46-d7f3-42f6-9bf9-62423e809555'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_GLkCVXamBfYgm4vzsWlfZLjV', 'function': {'arguments': '{}', 'name': 'list_all_blobs_in_container'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 430, 'total_tokens': 444, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ded0d14823', 'finish_reason': 'tool_calls', 'logprobs': None, 'content_filter_results': {}}, id='run-90eef732-475b-4c9e-bedd-34228181d861-0', tool_calls=[{'name': 'list_all_blobs_in_container', 'args': {}, 'id': 'call_GLkCVXamBfYgm4vzsWlfZLjV', 'type': 'tool_call'}], usage_metadata={'input_tokens': 430, 'output_tokens': 14, 'total_tokens': 444, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content='["GenAI Boot Camp-2025.pdf", "GenAI Boot Camp.pdf", "Screenshot 2025-03-08 at 9.03.56\u202fPM.png", "Screenshot 2025-03-08 at 9.21.06\u202fPM.png", "Screenshot 2025-03-08 at 9.31.16\u202fPM.png", "Screenshot 2025-03-09 at 11.17.27\u202fAM.png", "Screenshot 2025-03-09 at 11.17.37\u202fAM.png", "images.png", "medium-lord-krishna-photo-paper-poster-13-inch-x-19-inch-akl1958-original-imafxmetebe9s6sj.webp", "sreeni.png"]', name='list_all_blobs_in_container', id='d0cd95f6-7294-4178-99f5-3735ed3dd916', tool_call_id='call_GLkCVXamBfYgm4vzsWlfZLjV'), AIMessage(content='Here are the files listed in the blob:\n\n1. GenAI Boot Camp-2025.pdf\n2. GenAI Boot Camp.pdf\n3. Screenshot 2025-03-08 at 9.03.56 PM.png\n4. Screenshot 2025-03-08 at 9.21.06 PM.png\n5. Screenshot 2025-03-08 at 9.31.16 PM.png\n6. Screenshot 2025-03-09 at 11.17.27 AM.png\n7. Screenshot 2025-03-09 at 11.17.37 AM.png\n8. images.png\n9. medium-lord-krishna-photo-paper-poster-13-inch-x-19-inch-akl1958-original-imafxmetebe9s6sj.webp\n10. sreeni.png', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 173, 'prompt_tokens': 616, 'total_tokens': 789, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ded0d14823', 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {}}, id='run-fa3454f0-fb0c-4b25-b766-b085465efc72-0', usage_metadata={'input_tokens': 616, 'output_tokens': 173, 'total_tokens': 789, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}

*Enter a query: list files from directory *.
Processing request of type CallToolRequest
================================ Human Message =================================

list files from directory .
None
================================== Ai Message ==================================
Tool Calls:
list_files (call_dVTmUBKkAKrZBOZBL148y9K7)
Call ID: call_dVTmUBKkAKrZBOZBL148y9K7
Args:
path: .
None
================================= Tool Message =================================
Name: list_files

["swarm.py", "compare_thumbnails.py", "requirements_new.txt", "mcpclinet.py", "sreeni_friends.db", "krishna_new.png", "requirements.txt", "azure_blob.py", "debug_image.dat", "agents.py", "newfile.txt", "multi_server_client.py", ".env", "swarm.png", "thumbnail.png", "files", "mcpserver.py", "sreenisub", "workflow.png", "mcp_sse.py"]
None
================================== Ai Message ==================================

I requested to list my current project directory as shown below

Image description

Here are the files in the current directory:

  1. swarm.py
  2. compare_thumbnails.py
  3. requirements_new.txt
  4. mcpclinet.py
  5. sreeni_friends.db
  6. krishna_new.png
  7. requirements.txt
  8. azure_blob.py
  9. debug_image.dat
  10. agents.py
  11. newfile.txt
  12. multi_server_client.py
  13. .env
  14. swarm.png
  15. thumbnail.png
  16. files
  17. mcpserver.py
  18. sreenisub
  19. workflow.png
  20. mcp_sse.py None

Agent response:
{'messages': [HumanMessage(content='list files from directory .', additional_kwargs={}, response_metadata={}, id='158c3863-b6db-4afb-a5e4-0fcb1663b7c5'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_dVTmUBKkAKrZBOZBL148y9K7', 'function': {'arguments': '{"path":"."}', 'name': 'list_files'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 430, 'total_tokens': 444, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ded0d14823', 'finish_reason': 'tool_calls', 'logprobs': None, 'content_filter_results': {}}, id='run-e1f1a689-1a82-44d6-beb6-60940071f808-0', tool_calls=[{'name': 'list_files', 'args': {'path': '.'}, 'id': 'call_dVTmUBKkAKrZBOZBL148y9K7', 'type': 'tool_call'}], usage_metadata={'input_tokens': 430, 'output_tokens': 14, 'total_tokens': 444, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content='["swarm.py", "compare_thumbnails.py", "requirements_new.txt", "mcpclinet.py", "sreeni_friends.db", "krishna_new.png", "requirements.txt", "azure_blob.py", "debug_image.dat", "agents.py", "newfile.txt", "multi_server_client.py", ".env", "swarm.png", "thumbnail.png", "files", "mcpserver.py", "sreenisub", "workflow.png", "mcp_sse.py"]', name='list_files', id='0a38d19a-8879-4e5c-86df-caed6d881e4b', tool_call_id='call_dVTmUBKkAKrZBOZBL148y9K7'), AIMessage(content='Here are the files in the current directory:\n\n1. swarm.py\n2. compare_thumbnails.py\n3. requirements_new.txt\n4. mcpclinet.py\n5. sreeni_friends.db\n6. krishna_new.png\n7. requirements.txt\n8. azure_blob.py\n9. debug_image.dat\n10. agents.py\n11. newfile.txt\n12. multi_server_client.py\n13. .env\n14. swarm.png\n15. thumbnail.png\n16. files\n17. mcpserver.py\n18. sreenisub\n19. workflow.png\n20. mcp_sse.py', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 131, 'prompt_tokens': 556, 'total_tokens': 687, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_b705f0c291', 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {}}, id='run-161201cd-bbe3-4ac2-b58a-782202371585-0', usage_metadata={'input_tokens': 556, 'output_tokens': 131, 'total_tokens': 687, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}

Enter a query:

Conclusion: The Future of AI Integration with MCP

The Model Context Protocol represents a significant leap forward in how AI systems interact with the world's data. By establishing a universal standard for data connectivity, MCP addresses one of the most persistent challenges in AI development: creating seamless connections between powerful language models and the diverse range of external information sources they need to access.

As we've explored throughout this blog, MCP offers several transformative benefits:

  • Unified Integration: By providing a standardized way for AI to connect with databases, file systems, APIs, and cloud storage, MCP eliminates the need for custom integration solutions for each new data source.

  • Separation of Concerns: MCP servers can be developed and maintained independently from AI applications, allowing specialists to focus on their respective domains.

  • Enhanced Real-Time Capabilities: AI systems can now access up-to-date information without requiring retraining or complex workarounds.

  • Democratized Development: The open standard nature of MCP means that developers of all skill levels can create AI solutions that interact with external data sources.

Our implementations of MCP servers for database operations, file system access, real-time data streaming, and cloud storage integration demonstrate the versatility and practical applications of this protocol. These examples are just the beginning – as the MCP ecosystem grows, we can expect to see increasingly sophisticated interactions between AI systems and the digital world.

The comparison to USB-C is apt: just as this universal connector simplified hardware connectivity, MCP promises to standardize the way AI connects with data, removing unnecessary complexity and enabling new possibilities. For developers, businesses, and end-users alike, MCP represents a future where AI systems can seamlessly access, process, and act upon the information they need – marking a crucial step toward more capable and contextually aware artificial intelligence.

As this standard gains adoption across the industry, we encourage developers to explore how MCP can enhance their AI implementations and contribute to this emerging ecosystem. The age of fragmented AI data connections is coming to an end – MCP is ushering in a new era of standardized, efficient AI integration.

Thanks
Sreeni Ramadorai

Top comments (2)

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muthu_ganapathi profile image
Muthu ganapathi •

Good one

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sreeni5018 profile image
Seenivasa Ramadurai •

10Q Muthu

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