A fintech company has built a powerful financial analytics tool. The tool can analyze transaction patterns and provide investment recommendations. Now, this fintech wants to make the tool accessible to its customers’ AI assistants, like ChatGPT.

So a customer can say something like:

“Hey GPT, analyze my spending patterns for the past year and recommend investment options based on my financial goals.”

…and get a viable, trustworthy response.

• Before Model Context Protocol, this fintech would have had to build separate, complex integrations for each financial data source, authentication system, and AI assistant. Doing so presents significant obstacles to both feasibility and scalability.

With Model Context Protocol, this company can build a dedicated server that exposes its financial analytics as standardized tools. This allows any compatible AI assistant to securely analyze customer data and carry out requests without the need for separate integrations for each one.

A new paradigm? Yes, and not only for fintech. Let’s look at how Model Context Protocol really works.

What is Model Context Protocol?

Model Context Protocol (MCP) is a standardized communication framework that allows AI models to securely access external data sources and tools through a unified interface. Anthropic introduced MCP at the end of 2024, defining it as “a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments.”

MCP consists of three fundamental components: 

• Tools: Executable functions that the AI model can call to perform specific actions, such as querying a database or interacting with an API. Tools allow the model to take action and retrieve information dynamically.
• Resources: File-like structured data that the AI model can access directly. These function similarly to files or documents that provide contextual rules to follow.
• Prompts: Predefined templates that guide how the AI model interacts with the available tools and resources, helping to standardize interactions and optimize performance.

MCP uses JSON-RPC for communication, enabling standardized interaction between components, regardless of their underlying implementation details or programming language. It requires three core components to operate: host (the AI application); client (the part that communicates/translates between host and server); and server (the system being accessed, such as a sales or analytics platform).

Why MCP is such a breakthrough

MCP provides something so critical to realizing the value of LLMs: 

Context. 

In sales, for example, MCP could deeply enrich lead profiles or streamline workflows for sales reps. Guru explores a real-world example of this integration here.

Of course, modern enterprises are intent on creating as much value as possible with their AI initiatives. Today, 21% of organizations with GenAI in use “have fundamentally redesigned at least some workflows.” MCP provides a compelling means for doing just that.

That’s not all that the McKinsey report reveals. Today, while 72% of organizations are now utilizing AI solutions, more than 80% report seeing no tangible impact on enterprise-level earnings from their AI implementations. 

The primary challenge isn't the AI technology itself but the persistent data isolation that limits its effectiveness. MCP helps enterprises address these challenges by simplifying integration architecture, first and foremost. MCP transforms what was previously an “M×N” integration problem (each AI application needing separate connections to each data source) into a simpler M+N relationship.

MCP also enhances contextual awareness by enabling AI systems to access enterprise data in its native environment without requiring data migration or duplication. This contextual grounding allows models to deliver more accurate, relevant responses based on real-time business information rather than relying on training data alone. 

Real-world MCP use cases already abound

At a high level, you’ll be seeing MCP use cases in a few core areas: 

• Chatbots: Enterprise support systems that leverage MCP to securely access company knowledge bases, ticketing systems, and product documentation, providing employees and customers with consistent, accurate responses regardless of which AI model powers the interaction. Using MCP, you can now create chatbots that speak different languages with Gemini, Gemma, and Translation LLM. 
• Business Intelligence (BI) dashboards: AI-enhanced analytics interfaces that can query multiple data sources through a standardized protocol, enabling natural language exploration of business metrics and automated insights generation across previously siloed enterprise systems. BytePlus explores some of the possibilities of the BI dashboard use case. 
• Copilot tools: Microsoft recently introduced Model Context Protocol (MCP) in Copilot Studio,  which allows for the direct integration of AI apps and agents into a copilot environment. With this functionality, a team could create a unified design intelligence platform, for example, that connects various knowledge sources into a cohesive AI assistant for designs.

These are hardly the only new use cases made possible by MCP. For example, Shopify has released its own MCP server, which integrates Shopify’s GraphQL API for more comprehensive store management. Here are a few other potential use cases:

What if financial advisors could access real-time market data while consulting clients? By adding MCP server support, FinTech companies can now enable AI assistants that pull client portfolio information, market trends, and regulatory updates during client conversations.

What if automotive dealers could provide instant vehicle information through a chatbot? An MCP integration can give AI assistants access to inventory systems, vehicle specifications, financing options, and maintenance records in real time. 

What if smart home systems could adjust based on user behavior patterns? IoT providers can use MCP to connect their AI assistants to sensor networks, energy usage data, and user preferences. As a result, these assistants can create a more responsive home environment that anticipates needs, optimizes comforts, and reduces energy consumption. 

Moving forward, look for the interoperability of MCP to continue driving increased adoption. But integrating tools and context with agentic AI is only one side of the coin. The other side is inter-agent collaboration – AI agents talking to each other – which Google has taken the lead on with its Agent2Agent Protocol.

A2A makes it possible for two types of agents to communicate with each other: 

• Client agent: Formulates and communicates the task (for example, a human inputs a request into an interface like Google Agentspace)
• Remote agent: Executing tasks communicated by the client agent (for example, the Agentspace agent takes the human input and uses it to communicate with specialized agents for execution)

The ability of agents to coordinate and reason with each other, with full context and tool integration, opens a nearly endless world of possibilities for enterprises scaling with AI.

Is MCP your organization’s MVP? 

As a design and engineering services provider, our goal is to help organizations scale and digitally transform. MCP provides a standardized way to connect AI agents with existing data sources and business systems, without complex (and custom) integrations. 

This protocol can significantly reduce development time and maintenance overhead by automatically exposing your organization’s knowledge and workflows to AI assistants, all through a secure, governed framework. 

If your organization is struggling to realize the value of AI, Transcenda can help. Our team is actively exploring new use cases for MCP. Connect with us to learn more.