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MCP: When AI Gets Both Context and Connection

  • Writer: Aastha Thakker
    Aastha Thakker
  • Oct 30, 2025
  • 7 min read

MCP Gives AI Social Intelligence


When you meet different people throughout your day, you naturally adjust your tone and approach. You’re more casual with friends, more formal in meetings, and somewhere in between with the random person you meet on your way to the office. You don’t think about it, you just know how to read the room, correct. What if AI could do the same?


Model Context Protocol (MCP) gives AI that same ability. It’s like handing the AI a little note before each conversation, saying, “Hey, you’re helping someone code right now,” or “This person needs customer service help.” This way, the AI shows up prepared for the specific situation without awkwardly trying to figure out what’s happening.


It’s not about making AI memorize your entire history, just like you don’t tell your dentist your complete life story. It’s about giving just enough context, so the interaction feels natural and helpful for whatever you need in that moment.


MCP is what lets AI be a better conversation partner by understanding the social context of each interaction, just like humans naturally do.


Smart AI with Limited Access


Most people interact with AI through chatbots or virtual assistant tools that answer questions, summarize text, or offer weather updates. But these assistants often work with their hands tied. They don’t know your files, calendar, or company policies, not because they lack intelligence, but because they lack access.


When you ask an AI, “What meetings do I have today?” without MCP, it would say, “I don’t know your schedule.” With MCP, it can check your calendar system, see you have a 2 PM with marketing and a 4 PM client call, and tell you exactly what’s on your agenda, just like a human assistant with access to your calendar would.


That’s where MCP comes in.


What is MCP?


Anthropic created the Model Context Protocol (MCP) as an open-source solution to this access problem. At its core, MCP is:

  • A standardized way for AI to safely connect with your business systems and data.

  • The connector that lets AI check your CRM, company policies, or customer data when needed.

  • An open standard anyone can use, though it’s still evolving in the industry.

MCP is a client-server protocol that enables Large Language Models (LLMs) to discover, connect to, and interact with external tools and data sources through a standardized JSON-based messaging format.

Why MCP Matters?


MCP provides three critical technical capabilities that transform how LLMs interact with external systems:


  1. Tool Discovery: LLMs can automatically find available tools (similar to finding all apps on your phone)

  2. Schema Understanding: LLMs can parse the required input/output structure for each tool

  3. Standardized Communication: LLMs can call external tools using structured JSON requests and process responses

The key technical benefit is that MCP enables LLMs to interact with external tools in much the same way a human developer would call a REST API, but without requiring human intervention to write code or interpret results.


MCP vs. APIs


APIs (Application Programming Interfaces) and the Model Context Protocol (MCP) both provide ways to access external tools or services through structured data exchange, but they’re designed for very different users.


APIs (Application Programming Interfaces) are like specialized electrical outlets that let different software systems connect. For example, if we want to translate text using a translation service, we’d use code like this:

# Example - Not to Run
import requests
response = requests.post(
“https://api.translate.com/v1/translate",
json={“text”: “Hello”, “target_lang”: “es”},
headers = {“Authorization”: “Bearer xyz”}
)
print(“Status code:”, response.status_code)
print(“Response text:”, response.text) # ← check this first
try:
translated = response.json()
print(“Translation:”, translated[‘result’])
except ValueError as e:
print(“Failed to parse JSON:”, e)

As a developer using an API, you must:

  • Know the exact endpoint URL

  • Format the request correctly

  • Study documentation (often complex Swagger/OpenAPI specs)

  • Write code to handle the response

  • Handle errors and edge cases


MCP Approach for LLMs


MCP acts like a universal translator between AI models and external tools. When working with an LLM like Claude, GPT-4, or Llama 3.2, you can simply ask:


“Translate ‘Hello’ to Spanish.”


With MCP, the model can:

  1. Discover that the translation tool is available

  2. Understand what inputs it needs

  3. Format a proper request

  4. Call the translation API

  5. Interpret the results

  6. Present them in a human-friendly way


All without requiring a human to write a single line of code.


Feature Comparison


The “Middleman Removal”

In traditional setups, there’s always a human in the loop; the developer. If you want your system to check emails, someone has to code a connector to Gmail’s API. If you want to book a meeting, you need an integration with your calendar. Every new task requires new code.


This is the middleman problem.


  • Chef: Symbolizes the technical system or backend. He speaks a different “language” (like code or system instructions) and knows how to cook (i.e., process tasks) but can’t communicate directly with customers.

  • AI Waiter: Represents MCP. This robot understands both the customer’s request and the chef’s language. It translates the customer’s plain-language order into instructions the chef understands, then delivers the correct result.

  • Customer: Stands for the user (like a business stakeholder or non-technical person). Instead of having to understand code or communicate through a human developer, they just express their needs in natural language.

No more back-and-forth coding. No more one-off integrations. Just direct, task-based interactions.


MCP: The Universal Connector for AI


The USB-C port on your laptop doesn’t care whether you’re plugging in a charger, external monitor, or microphone. It just works — that’s the magic of standardization. MCP brings this same “just works” philosophy to AI.


When you connect a language model to your CRM, internal wiki, or scheduling app using MCP, you’re not writing custom code for each tool. The model sees a standard format — a predictable, structured way to interact with every system. It’s plug-and-play for intelligence.


The breakthrough is that MCP removes the need for human developers to create custom integrations for every task. Instead of:


It becomes:


This allows AI to perform practical tasks like checking calendars, searching databases, or filing expenses without requiring custom programming for each task. It transforms AI from “smart but isolated” to “smart and connected.”


MCP Technical Architecture


MCP (Model Context Protocol) uses a client-server design that allows applications to connect with multiple data sources or tools through a standardized communication approach.


Core Components

  • MCP Hosts: These are applications (like an AI assistant) that need to access external data or functionality.

  • MCP Clients: Components within the host that manage individual connections to MCP servers.

  • MCP Servers: Lightweight services that provide specific functionality (like file access, database queries, or API integration).

  • Data Sources: The actual information that servers can access, either locally (files/databases) or remotely (web APIs).

How It Works


1. Clients: The app or tool where the AI lives and works (like Cursor or Claude Desktop).

What Clients Do:

  • Ask the MCP server: “What tools/resources can I use?”

  • Pass those tools to the AI model: So, the model knows what’s available.

  • Send the AI’s requests (like “search this file” or “fetch a note”) back to the server and return results.

  • Keep the conversation flowing: between you (user), the AI, and the MCP server.

2. MCP Servers These are the “middlemen” between the AI and real-world tools.


What MCP Servers Do:

  • Offer a standard toolset in a format the AI can understand (JSON-RPC).

  • Convert normal APIs (like Notion, Google Calendar) into AI-usable capabilities without modifying them.

  • Handle authentication, tool definitions, and communication rules.


3. Service Providers


These are the actual tools or platforms you want the AI to use — like Discord, Notion, Figma, Google Drive, etc.


What’s special?


They don’t have to change anything to work with MCP.


MCP Servers talk to them using their original API and pass responses back to the AI in a way it understands.


Service Providers are like restaurants. MCP Servers are the waiters translating your order. Clients are the menu and table setup. You are the customer, and the AI is the chef.


How it works:


1. Connection Setup:

  • Client introduces itself with version info and capabilities.

  • The server responds with its own details.

  • Client confirms connection is active.

2. Working Together:

  • Messages flow back and forth between client and server

  • Requests can be two-way (requiring responses) or one-way notifications

  • Structured as JSON messages with standardized fields

3. Ending Connections: Either side can terminate the connection when finished Implementation Challenges


While MCP simplifies AI integration, implementers should be aware of these technical considerations:

  1. Authentication Complexity: Though MCP handles authentication, secure credential management must still be configured

  2. Latency Management: Multiple external system calls can introduce performance bottlenecks

  3. Error Handling: Failures in external systems need proper fallback strategies

  4. Schema Definition: Accurate tool descriptions are critical for proper AI understanding

Does MCP Train Models on Your Data?


This is an important misconception to clarify: MCP does not train or fine-tune AI models on your data. MCP is fundamentally a communication protocol, not a training mechanism.

When an AI uses MCP to access your company database or personal calendar, it’s reading that information at the moment you ask for it, similar to how you might look up information on a website. The underlying AI model itself doesn’t change or adapt based on this access. Your data isn’t being used to modify the model’s parameters or capabilities.

It’s like the difference between teaching someone a new skill (training) versus giving them a reference book to look up information (MCP):


  • Training: Changes what the model knows.

  • MCP: Temporarily provides access to data, just for the current task.


From a technical perspective, MCP establishes a runtime connection between the AI and external systems through well-defined schemas and authentication protocols. The data flows through this connection only during active requests and isn’t incorporated into the model’s weights or parameters. This maintains a clear separation between the model’s capabilities and the data it can temporarily access.


This open-source protocol eliminates the need for custom code while maintaining data privacy, transforming AI from isolated conversation partners into connected helpers that can discover, access, and interact with your digital world, turning “I don’t know” into “I’ve got this.” See you next Thursday with hands-on!

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