Understanding Model Context Protocol (MCP) 

Introduction

AI agents are increasingly capable — they can analyse data, retrieve information, write code, and carry-on natural conversations. But when it's time to execute tasks in real-world systems, they often fall short. 

The reason? Most enterprise tools and infrastructure still require ad hoc, isolated integrations. 

Model Context Protocol (MCP) solves this. It provides a unified, secure interface that allows AI agents to seamlessly interact with systems, data, and services — no custom code, no manual workarounds. It bridges the missing link between intelligence and execution. 

In this blog, lets delve deeper into what is MCP, its capabilities and how AI Agents are powered by MCP increases efficiency.  

What is Model Context Protocol (MCP)?  

Model Context Definition 

Model Context Protocol (MCP) is an open standard that defines how applications provide context to large language models (LLMs). MCP gives AI agents a consistent, structured way to connect with tools, services, and data, regardless of where they reside or how they’re built. MCP standardizes how AI models interface with external systems. The result: interoperability without custom code. 

As featured in Forbes, MCP represents a significant step forward in how AI agents operate. It enables agents not just to respond to prompts, but to carry out meaningful, multi-step tasks — such as retrieving information, summarizing documents, or storing content — with minimal setup. 

Traditionally, each of these capabilities required APIs, custom logic, and manual integration. MCP streamlines that. With a single protocol, agents can send structured requests to any MCP-compatible system, receive responses in real time, and even orchestrate multiple tools in sequence — all without needing preprogrammed knowledge of the systems involved. 

In short, MCP replaces fragmented integrations with a unified, real-time framework purpose-built for autonomous AI. 

Model Context Protocol (MCP) Architecture:   

Image: modelcontextprotocol.io

How does Model Context Protocol (MCP) work? 

At the core of MCP is a simple yet powerful architecture: 

• The MCP Host (on the left) represents the AI-driven application — such as An AI agent interface like Konverso AI Agent platform, or any tool functioning as an AI agent.
  
  
• The host communicates with one or more MCP Servers, each exposing a specific tool, service, or resource.

• Some servers interface with local systems — like your file system or an on-device database.

• Others connect to remote resources, such as web APIs or cloud-based services. 

• All interactions occur over the standardized MCP Protocol, which ensures interoperability, consistent formatting, and reliable, structured communication between components.
   

Model Context Protocol (MCP) Capabilities 

1. Resources

These resources can take many forms — essentially, any type of data that a server wants to make accessible. Examples include: 

• File content 

• Records from databases 

• Responses from APIs 

• Real-time system data 

• Screenshots or images 

• Application logs 

• And more 

Each resource is assigned a distinct URI and can carry either textual or binary information, allowing clients to retrieve and utilize it seamlessly within an AI-driven workflow. 

2. Tools

In MCP, tools serve as a core mechanism that allows servers to expose actions and capabilities to clients. They enable AI models to interact with external systems, execute operations, and drive real-world outcomes. 

These tools are intended to be AI-accessible — meaning they're designed to be called by the model itself, often with human oversight or approval as needed. 

By standardizing how functionality is exposed and accessed, MCP tools make it easier for LLMs to go beyond conversation and take meaningful, automated action 

3. Prompts

Prompts in the Model Context Protocol (MCP) serve as configurable templates that streamline and standardize interactions between users, AI models, and tools. These prompts are defined on the server side and made accessible to client applications, allowing both users and LLMs to easily invoke predefined workflows. 

• A key design principle is user control — prompts are intentionally exposed by servers so that users can discover, select, and trigger them as needed. 

• Prompts can support a variety of advanced features, including: 

• Accepting dynamic inputs at runtime 

• Incorporating contextual data from connected resources 

• Chaining steps across multiple tools or actions 

• Enabling structured, guided workflows 
  

Together, these capabilities enable AI agents to not only think but act — with consistency, autonomy, and precision. But why is this important now? Let’s explore the broader significance of MCP in real-world AI systems. 

Model Context Protocol (MCP) supports OAuth  

MCP supports OAuth 2.1 authentication, enabling clients and servers to securely delegate authorization. With this enhancement, MCP servers implementing the new specification can authorize users to perform specific actions on services that already support OAuth, such as HubSpot, Google Calendar, GitHub, and others. 

What does this mean for you? 

• Enhanced Security: Clients benefit from stronger built-in protection against common security threats, significantly raising the security baseline from the start. 

• Simplified Configuration: The specification promotes Metadata Discovery, allowing servers to automatically expose their OAuth endpoints. This minimizes manual setup and reduces the risk of configuration errors. 
  
  
• Seamless Identity Integration: MCP explicitly supports scenarios where user authentication is handled by a trusted third-party identity provider, such as Auth0. This allows the MCP server to delegate the login process while maintaining secure, centralized authorization.
   

Why use Model Context Protocol (MCP)? 

The Model Context Protocol (MCP) enables you to build robust agents and sophisticated workflows on top of large language models (LLMs). Since LLMs often need to interact with external data and tools, MCP provides the infrastructure to make those connections seamless. 

Key benefits include: 

1. A growing ecosystem of pre-built integrations — giving your LLM instant access to commonly used tools and services.
   
   
2. Flexibility to switch LLM providers or vendors — without re-architecting your workflows.
   
   
3. Interfacing with Multiple LLMs - Without a shared communication layer, coordination between models can be clumsy or ineffective. The MCP Server provides that shared layer — enabling models to exchange information fluidly and collaborate in real time.
   
   
4. Routing and Transforming Data- Without proper routing and transformation, inputs can become outdated or inconsistent. The MCP Server ensures data is always where it needs to be — cleaned, formatted, and ready for consumption by any compatible tool or model in the pipeline. 
   
   
5. MCP support OAuth authentication to make sure that each user is securely identified by the server. 
   
   

Conclusion

Model Context Protocol is a foundational shift in how AI agents operate — moving from isolated, static prompts to dynamic, tool-connected systems capable of real-world action.  

Konverso is at the forefront of MCP adoption, with its platform already integrated with a growing number of MCP Servers — including HubSpot, Zapier, GitHub, Make, Outlook, and more. These connections enable AI agents on Konverso to perform real-world actions across a wide range of tools, out of the box. 

Curious to see it in action? Watch the demo below to explore how an MCP-powered agent works seamlessly with HubSpot. 

Schedule a demo today and supercharge your teams with personalized AI Agents. 

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Sources:

https://www.forbes.com/sites/janakirammsv/2024/11/30/why-anthropics-model-context-protocol-is-a-big-step-in-the-evolution-of-ai-agents/ https://medium.com/@elisowski/mcp-explained-the-new-standard-connecting-ai-to-everything-79c5a1c98288  https://modelcontextprotocol.io/introduction https://auth0.com/blog/an-introduction-to-mcp-and-authorization/